{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "253fbcd2-ba84-4299-b9ac-6bdebe0d8050",
   "metadata": {},
   "source": [
    "# An Analysis on Tourism"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a7b1cd-6c6a-4db2-bd1f-9f51dda17226",
   "metadata": {},
   "source": [
    "## Predition on Total Number of Tourists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ce1df1-725c-4273-afe2-47058d87c68f",
   "metadata": {},
   "source": [
    "### Clean the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae7c5bd-c678-4f34-be9c-ce9ff707d198",
   "metadata": {},
   "source": [
    "We collected data from 2010 to 2023 for various provincial-level administrative divisions in China (excluding Hong Kong, Macao, Taiwan), with indicators including the total number of tourists, the total number of tourist attractions, and urban public infrastructure that affects the volume of tourists. \n",
    "\n",
    "The data comes from China Statistical Yearbook, China Tourism Statistical Yearbook, China Culture Relics and Tourism Statistical Yearbook, and tourism sampling survey materials, collated by EPS data.\n",
    "\n",
    "First, we clean the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d8a4d8e6-f816-4d3d-9561-4f86620f3eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>Number of employees in tourism industry (person)</th>\n",
       "      <th>Number of institutions (number)</th>\n",
       "      <th>Number of rooms in star rated hotels (rooms/set)</th>\n",
       "      <th>Number of star rated hotels (number)</th>\n",
       "      <th>Total number of tourist attractions (units)</th>\n",
       "      <th>Total number of tourists (100 million)</th>\n",
       "      <th>Cleaning area (10000 square meters)</th>\n",
       "      <th>Consumer price index (last year=100)</th>\n",
       "      <th>Highway mileage (km)</th>\n",
       "      <th>Museums</th>\n",
       "      <th>Number of taxis (vehicles)</th>\n",
       "      <th>Performing arts groups (number)</th>\n",
       "      <th>Public toilets per 10000 people</th>\n",
       "      <th>Railway operating mileage (km)</th>\n",
       "      <th>Urban road lighting (thousands)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Anhui</th>\n",
       "      <th>2010</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44265.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>0.71475</td>\n",
       "      <td>17339.00</td>\n",
       "      <td>103.13920</td>\n",
       "      <td>149382.000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>1255.0</td>\n",
       "      <td>2.54531</td>\n",
       "      <td>2900.000</td>\n",
       "      <td>548.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>55840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43479.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>1.06000</td>\n",
       "      <td>18315.00</td>\n",
       "      <td>105.56246</td>\n",
       "      <td>149535.000</td>\n",
       "      <td>131.0</td>\n",
       "      <td>36525.0</td>\n",
       "      <td>1294.0</td>\n",
       "      <td>2.44509</td>\n",
       "      <td>3100.000</td>\n",
       "      <td>659.485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44796.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>1.80000</td>\n",
       "      <td>22229.00</td>\n",
       "      <td>102.26080</td>\n",
       "      <td>165157.000</td>\n",
       "      <td>141.0</td>\n",
       "      <td>37142.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>2.31572</td>\n",
       "      <td>3300.000</td>\n",
       "      <td>722.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45279.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>1.80000</td>\n",
       "      <td>24081.00</td>\n",
       "      <td>102.40924</td>\n",
       "      <td>173763.000</td>\n",
       "      <td>154.0</td>\n",
       "      <td>37833.0</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2.26129</td>\n",
       "      <td>3500.000</td>\n",
       "      <td>766.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46328.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>1.76000</td>\n",
       "      <td>26370.00</td>\n",
       "      <td>101.60000</td>\n",
       "      <td>174373.000</td>\n",
       "      <td>164.0</td>\n",
       "      <td>38792.0</td>\n",
       "      <td>988.0</td>\n",
       "      <td>2.22846</td>\n",
       "      <td>3548.443</td>\n",
       "      <td>809.764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">nationwide</th>\n",
       "      <th>2019</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38943.0</td>\n",
       "      <td>1203831.0</td>\n",
       "      <td>8920.0</td>\n",
       "      <td>12402.0</td>\n",
       "      <td>64.74993</td>\n",
       "      <td>922124.15</td>\n",
       "      <td>102.90000</td>\n",
       "      <td>5012495.792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1102470.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.92711</td>\n",
       "      <td>139926.388</td>\n",
       "      <td>28655.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40682.0</td>\n",
       "      <td>1160622.0</td>\n",
       "      <td>8423.0</td>\n",
       "      <td>13332.0</td>\n",
       "      <td>32.37000</td>\n",
       "      <td>975595.00</td>\n",
       "      <td>102.50000</td>\n",
       "      <td>5198120.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1113153.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.07000</td>\n",
       "      <td>146330.000</td>\n",
       "      <td>30485.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31001.0</td>\n",
       "      <td>1120947.0</td>\n",
       "      <td>7676.0</td>\n",
       "      <td>14196.0</td>\n",
       "      <td>35.43000</td>\n",
       "      <td>1034211.20</td>\n",
       "      <td>100.90000</td>\n",
       "      <td>5280708.078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1391315.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.29107</td>\n",
       "      <td>150739.348</td>\n",
       "      <td>32459.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31001.0</td>\n",
       "      <td>1114141.0</td>\n",
       "      <td>7337.0</td>\n",
       "      <td>14917.0</td>\n",
       "      <td>26.33000</td>\n",
       "      <td>1081813.85</td>\n",
       "      <td>102.00000</td>\n",
       "      <td>5354837.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1362041.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.42822</td>\n",
       "      <td>154907.000</td>\n",
       "      <td>33524.880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>NaN</td>\n",
       "      <td>39580.0</td>\n",
       "      <td>1067203.0</td>\n",
       "      <td>7245.0</td>\n",
       "      <td>15721.0</td>\n",
       "      <td>57.54000</td>\n",
       "      <td>1126852.81</td>\n",
       "      <td>100.20000</td>\n",
       "      <td>5436845.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367416.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>158737.300</td>\n",
       "      <td>34817.012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>448 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "variable         Number of employees in tourism industry (person)  \\\n",
       "region     year                                                     \n",
       "Anhui      2010                                               NaN   \n",
       "           2011                                           55840.0   \n",
       "           2012                                               NaN   \n",
       "           2013                                               NaN   \n",
       "           2014                                               NaN   \n",
       "...                                                           ...   \n",
       "nationwide 2019                                               NaN   \n",
       "           2020                                               NaN   \n",
       "           2021                                               NaN   \n",
       "           2022                                               NaN   \n",
       "           2023                                               NaN   \n",
       "\n",
       "variable         Number of institutions (number)  \\\n",
       "region     year                                    \n",
       "Anhui      2010                              NaN   \n",
       "           2011                              NaN   \n",
       "           2012                              NaN   \n",
       "           2013                              NaN   \n",
       "           2014                              NaN   \n",
       "...                                          ...   \n",
       "nationwide 2019                          38943.0   \n",
       "           2020                          40682.0   \n",
       "           2021                          31001.0   \n",
       "           2022                          31001.0   \n",
       "           2023                          39580.0   \n",
       "\n",
       "variable         Number of rooms in star rated hotels (rooms/set)  \\\n",
       "region     year                                                     \n",
       "Anhui      2010                                           44265.0   \n",
       "           2011                                           43479.0   \n",
       "           2012                                           44796.0   \n",
       "           2013                                           45279.0   \n",
       "           2014                                           46328.0   \n",
       "...                                                           ...   \n",
       "nationwide 2019                                         1203831.0   \n",
       "           2020                                         1160622.0   \n",
       "           2021                                         1120947.0   \n",
       "           2022                                         1114141.0   \n",
       "           2023                                         1067203.0   \n",
       "\n",
       "variable         Number of star rated hotels (number)  \\\n",
       "region     year                                         \n",
       "Anhui      2010                                 417.0   \n",
       "           2011                                 390.0   \n",
       "           2012                                 383.0   \n",
       "           2013                                 377.0   \n",
       "           2014                                 367.0   \n",
       "...                                               ...   \n",
       "nationwide 2019                                8920.0   \n",
       "           2020                                8423.0   \n",
       "           2021                                7676.0   \n",
       "           2022                                7337.0   \n",
       "           2023                                7245.0   \n",
       "\n",
       "variable         Total number of tourist attractions (units)  \\\n",
       "region     year                                                \n",
       "Anhui      2010                                        276.0   \n",
       "           2011                                        377.0   \n",
       "           2012                                        397.0   \n",
       "           2013                                        444.0   \n",
       "           2014                                        483.0   \n",
       "...                                                      ...   \n",
       "nationwide 2019                                      12402.0   \n",
       "           2020                                      13332.0   \n",
       "           2021                                      14196.0   \n",
       "           2022                                      14917.0   \n",
       "           2023                                      15721.0   \n",
       "\n",
       "variable         Total number of tourists (100 million)  \\\n",
       "region     year                                           \n",
       "Anhui      2010                                 0.71475   \n",
       "           2011                                 1.06000   \n",
       "           2012                                 1.80000   \n",
       "           2013                                 1.80000   \n",
       "           2014                                 1.76000   \n",
       "...                                                 ...   \n",
       "nationwide 2019                                64.74993   \n",
       "           2020                                32.37000   \n",
       "           2021                                35.43000   \n",
       "           2022                                26.33000   \n",
       "           2023                                57.54000   \n",
       "\n",
       "variable         Cleaning area (10000 square meters)  \\\n",
       "region     year                                        \n",
       "Anhui      2010                             17339.00   \n",
       "           2011                             18315.00   \n",
       "           2012                             22229.00   \n",
       "           2013                             24081.00   \n",
       "           2014                             26370.00   \n",
       "...                                              ...   \n",
       "nationwide 2019                            922124.15   \n",
       "           2020                            975595.00   \n",
       "           2021                           1034211.20   \n",
       "           2022                           1081813.85   \n",
       "           2023                           1126852.81   \n",
       "\n",
       "variable         Consumer price index (last year=100)  Highway mileage (km)  \\\n",
       "region     year                                                               \n",
       "Anhui      2010                             103.13920            149382.000   \n",
       "           2011                             105.56246            149535.000   \n",
       "           2012                             102.26080            165157.000   \n",
       "           2013                             102.40924            173763.000   \n",
       "           2014                             101.60000            174373.000   \n",
       "...                                               ...                   ...   \n",
       "nationwide 2019                             102.90000           5012495.792   \n",
       "           2020                             102.50000           5198120.000   \n",
       "           2021                             100.90000           5280708.078   \n",
       "           2022                             102.00000           5354837.000   \n",
       "           2023                             100.20000           5436845.000   \n",
       "\n",
       "variable         Museums  Number of taxis (vehicles)  \\\n",
       "region     year                                        \n",
       "Anhui      2010    120.0                     36681.0   \n",
       "           2011    131.0                     36525.0   \n",
       "           2012    141.0                     37142.0   \n",
       "           2013    154.0                     37833.0   \n",
       "           2014    164.0                     38792.0   \n",
       "...                  ...                         ...   \n",
       "nationwide 2019      NaN                   1102470.0   \n",
       "           2020      NaN                   1113153.0   \n",
       "           2021      NaN                   1391315.0   \n",
       "           2022      NaN                   1362041.0   \n",
       "           2023      NaN                   1367416.0   \n",
       "\n",
       "variable         Performing arts groups (number)  \\\n",
       "region     year                                    \n",
       "Anhui      2010                           1255.0   \n",
       "           2011                           1294.0   \n",
       "           2012                           1015.0   \n",
       "           2013                            991.0   \n",
       "           2014                            988.0   \n",
       "...                                          ...   \n",
       "nationwide 2019                              NaN   \n",
       "           2020                              NaN   \n",
       "           2021                              NaN   \n",
       "           2022                              NaN   \n",
       "           2023                              NaN   \n",
       "\n",
       "variable         Public toilets per 10000 people  \\\n",
       "region     year                                    \n",
       "Anhui      2010                          2.54531   \n",
       "           2011                          2.44509   \n",
       "           2012                          2.31572   \n",
       "           2013                          2.26129   \n",
       "           2014                          2.22846   \n",
       "...                                          ...   \n",
       "nationwide 2019                          2.92711   \n",
       "           2020                          3.07000   \n",
       "           2021                          3.29107   \n",
       "           2022                          3.42822   \n",
       "           2023                          3.50000   \n",
       "\n",
       "variable         Railway operating mileage (km)  \\\n",
       "region     year                                   \n",
       "Anhui      2010                        2900.000   \n",
       "           2011                        3100.000   \n",
       "           2012                        3300.000   \n",
       "           2013                        3500.000   \n",
       "           2014                        3548.443   \n",
       "...                                         ...   \n",
       "nationwide 2019                      139926.388   \n",
       "           2020                      146330.000   \n",
       "           2021                      150739.348   \n",
       "           2022                      154907.000   \n",
       "           2023                      158737.300   \n",
       "\n",
       "variable         Urban road lighting (thousands)  \n",
       "region     year                                   \n",
       "Anhui      2010                          548.635  \n",
       "           2011                          659.485  \n",
       "           2012                          722.017  \n",
       "           2013                          766.222  \n",
       "           2014                          809.764  \n",
       "...                                          ...  \n",
       "nationwide 2019                        28655.982  \n",
       "           2020                        30485.600  \n",
       "           2021                        32459.300  \n",
       "           2022                        33524.880  \n",
       "           2023                        34817.012  \n",
       "\n",
       "[448 rows x 15 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge\n",
    "import pandas as pd\n",
    "scenic_spots=pd.read_excel(\"scenic_spots.xlsx\", \"sheet1\",)\n",
    "other=pd.read_excel(\"other.xlsx\", \"sheet1\",)\n",
    "other=other.pivot_table(index=['region','year'],columns='variable',values='value')\n",
    "scenic_spots=scenic_spots.pivot_table(index=['region','year'],columns='variable',values='value')\n",
    "df=pd.merge(scenic_spots,other,on=['region','year'],how='outer')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2483d706-061c-4ca0-9e39-46dde0f31431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "variable\n",
       "Number of employees in tourism industry (person)    416\n",
       "Number of institutions (number)                     288\n",
       "Number of rooms in star rated hotels (rooms/set)      0\n",
       "Number of star rated hotels (number)                  0\n",
       "Total number of tourist attractions (units)           1\n",
       "Total number of tourists (100 million)                3\n",
       "Cleaning area (10000 square meters)                   0\n",
       "Consumer price index (last year=100)                  3\n",
       "Highway mileage (km)                                  0\n",
       "Museums                                              14\n",
       "Number of taxis (vehicles)                            0\n",
       "Performing arts groups (number)                      14\n",
       "Public toilets per 10000 people                       0\n",
       "Railway operating mileage (km)                        0\n",
       "Urban road lighting (thousands)                       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c49f3eff-419e-437a-a5f5-5bd78c337e00",
   "metadata": {},
   "source": [
    "The first two columns have too many missing values, so we drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "698e9700-16c3-4c5a-bd40-eecdc82c53a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Number of rooms in star rated hotels (rooms/set)',\n",
       "       'Number of star rated hotels (number)',\n",
       "       'Total number of tourist attractions (units)',\n",
       "       'Total number of tourists (100 million)',\n",
       "       'Cleaning area (10000 square meters)',\n",
       "       'Consumer price index (last year=100)', 'Highway mileage (km)',\n",
       "       'Museums', 'Number of taxis (vehicles)',\n",
       "       'Performing arts groups (number)', 'Public toilets per 10000 people',\n",
       "       'Railway operating mileage (km)', 'Urban road lighting (thousands)'],\n",
       "      dtype='object', name='variable')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop(['Number of employees in tourism industry (person)','Number of institutions (number)'],axis=1)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec8f3ec-7fa5-4a29-bd73-72afeb5fe53f",
   "metadata": {},
   "source": [
    "The above variables are what we will actually use in the analysis. We drop the rest of the missing values and the nationwide data, which has too many missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a534e6ba-6b99-429d-a27f-871d94a10598",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>Number of rooms in star rated hotels (rooms/set)</th>\n",
       "      <th>Number of star rated hotels (number)</th>\n",
       "      <th>Total number of tourist attractions (units)</th>\n",
       "      <th>Total number of tourists (100 million)</th>\n",
       "      <th>Cleaning area (10000 square meters)</th>\n",
       "      <th>Consumer price index (last year=100)</th>\n",
       "      <th>Highway mileage (km)</th>\n",
       "      <th>Museums</th>\n",
       "      <th>Number of taxis (vehicles)</th>\n",
       "      <th>Performing arts groups (number)</th>\n",
       "      <th>Public toilets per 10000 people</th>\n",
       "      <th>Railway operating mileage (km)</th>\n",
       "      <th>Urban road lighting (thousands)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Anhui</th>\n",
       "      <th>2010</th>\n",
       "      <td>44265.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>0.71475</td>\n",
       "      <td>17339.00</td>\n",
       "      <td>103.13920</td>\n",
       "      <td>149382.000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>1255.0</td>\n",
       "      <td>2.54531</td>\n",
       "      <td>2900.000</td>\n",
       "      <td>548.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>43479.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>1.06000</td>\n",
       "      <td>18315.00</td>\n",
       "      <td>105.56246</td>\n",
       "      <td>149535.000</td>\n",
       "      <td>131.0</td>\n",
       "      <td>36525.0</td>\n",
       "      <td>1294.0</td>\n",
       "      <td>2.44509</td>\n",
       "      <td>3100.000</td>\n",
       "      <td>659.485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>44796.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>1.80000</td>\n",
       "      <td>22229.00</td>\n",
       "      <td>102.26080</td>\n",
       "      <td>165157.000</td>\n",
       "      <td>141.0</td>\n",
       "      <td>37142.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>2.31572</td>\n",
       "      <td>3300.000</td>\n",
       "      <td>722.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>45279.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>1.80000</td>\n",
       "      <td>24081.00</td>\n",
       "      <td>102.40924</td>\n",
       "      <td>173763.000</td>\n",
       "      <td>154.0</td>\n",
       "      <td>37833.0</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2.26129</td>\n",
       "      <td>3500.000</td>\n",
       "      <td>766.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>46328.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>1.76000</td>\n",
       "      <td>26370.00</td>\n",
       "      <td>101.60000</td>\n",
       "      <td>174373.000</td>\n",
       "      <td>164.0</td>\n",
       "      <td>38792.0</td>\n",
       "      <td>988.0</td>\n",
       "      <td>2.22846</td>\n",
       "      <td>3548.443</td>\n",
       "      <td>809.764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Zhejiang</th>\n",
       "      <th>2019</th>\n",
       "      <td>89507.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>798.0</td>\n",
       "      <td>5.12309</td>\n",
       "      <td>47185.56</td>\n",
       "      <td>102.90000</td>\n",
       "      <td>121812.898</td>\n",
       "      <td>366.0</td>\n",
       "      <td>39341.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>3.01981</td>\n",
       "      <td>2842.157</td>\n",
       "      <td>1709.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>84286.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>827.0</td>\n",
       "      <td>2.95000</td>\n",
       "      <td>53870.00</td>\n",
       "      <td>102.30000</td>\n",
       "      <td>123080.000</td>\n",
       "      <td>406.0</td>\n",
       "      <td>39750.0</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>3.05000</td>\n",
       "      <td>3159.000</td>\n",
       "      <td>1831.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>76545.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>909.0</td>\n",
       "      <td>3.24000</td>\n",
       "      <td>57716.68</td>\n",
       "      <td>101.50000</td>\n",
       "      <td>123885.350</td>\n",
       "      <td>425.0</td>\n",
       "      <td>43838.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>2.98016</td>\n",
       "      <td>3345.187</td>\n",
       "      <td>1890.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>73510.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>929.0</td>\n",
       "      <td>2.42000</td>\n",
       "      <td>58712.35</td>\n",
       "      <td>102.20000</td>\n",
       "      <td>122917.737</td>\n",
       "      <td>432.0</td>\n",
       "      <td>44092.0</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>3.13469</td>\n",
       "      <td>3711.532</td>\n",
       "      <td>1982.612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>68079.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>947.0</td>\n",
       "      <td>5.23000</td>\n",
       "      <td>64727.88</td>\n",
       "      <td>100.30000</td>\n",
       "      <td>121408.000</td>\n",
       "      <td>206.0</td>\n",
       "      <td>43467.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>2.89298</td>\n",
       "      <td>3852.700</td>\n",
       "      <td>2000.926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>430 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "variable       Number of rooms in star rated hotels (rooms/set)  \\\n",
       "region   year                                                     \n",
       "Anhui    2010                                           44265.0   \n",
       "         2011                                           43479.0   \n",
       "         2012                                           44796.0   \n",
       "         2013                                           45279.0   \n",
       "         2014                                           46328.0   \n",
       "...                                                         ...   \n",
       "Zhejiang 2019                                           89507.0   \n",
       "         2020                                           84286.0   \n",
       "         2021                                           76545.0   \n",
       "         2022                                           73510.0   \n",
       "         2023                                           68079.0   \n",
       "\n",
       "variable       Number of star rated hotels (number)  \\\n",
       "region   year                                         \n",
       "Anhui    2010                                 417.0   \n",
       "         2011                                 390.0   \n",
       "         2012                                 383.0   \n",
       "         2013                                 377.0   \n",
       "         2014                                 367.0   \n",
       "...                                             ...   \n",
       "Zhejiang 2019                                 528.0   \n",
       "         2020                                 500.0   \n",
       "         2021                                 453.0   \n",
       "         2022                                 437.0   \n",
       "         2023                                 410.0   \n",
       "\n",
       "variable       Total number of tourist attractions (units)  \\\n",
       "region   year                                                \n",
       "Anhui    2010                                        276.0   \n",
       "         2011                                        377.0   \n",
       "         2012                                        397.0   \n",
       "         2013                                        444.0   \n",
       "         2014                                        483.0   \n",
       "...                                                    ...   \n",
       "Zhejiang 2019                                        798.0   \n",
       "         2020                                        827.0   \n",
       "         2021                                        909.0   \n",
       "         2022                                        929.0   \n",
       "         2023                                        947.0   \n",
       "\n",
       "variable       Total number of tourists (100 million)  \\\n",
       "region   year                                           \n",
       "Anhui    2010                                 0.71475   \n",
       "         2011                                 1.06000   \n",
       "         2012                                 1.80000   \n",
       "         2013                                 1.80000   \n",
       "         2014                                 1.76000   \n",
       "...                                               ...   \n",
       "Zhejiang 2019                                 5.12309   \n",
       "         2020                                 2.95000   \n",
       "         2021                                 3.24000   \n",
       "         2022                                 2.42000   \n",
       "         2023                                 5.23000   \n",
       "\n",
       "variable       Cleaning area (10000 square meters)  \\\n",
       "region   year                                        \n",
       "Anhui    2010                             17339.00   \n",
       "         2011                             18315.00   \n",
       "         2012                             22229.00   \n",
       "         2013                             24081.00   \n",
       "         2014                             26370.00   \n",
       "...                                            ...   \n",
       "Zhejiang 2019                             47185.56   \n",
       "         2020                             53870.00   \n",
       "         2021                             57716.68   \n",
       "         2022                             58712.35   \n",
       "         2023                             64727.88   \n",
       "\n",
       "variable       Consumer price index (last year=100)  Highway mileage (km)  \\\n",
       "region   year                                                               \n",
       "Anhui    2010                             103.13920            149382.000   \n",
       "         2011                             105.56246            149535.000   \n",
       "         2012                             102.26080            165157.000   \n",
       "         2013                             102.40924            173763.000   \n",
       "         2014                             101.60000            174373.000   \n",
       "...                                             ...                   ...   \n",
       "Zhejiang 2019                             102.90000            121812.898   \n",
       "         2020                             102.30000            123080.000   \n",
       "         2021                             101.50000            123885.350   \n",
       "         2022                             102.20000            122917.737   \n",
       "         2023                             100.30000            121408.000   \n",
       "\n",
       "variable       Museums  Number of taxis (vehicles)  \\\n",
       "region   year                                        \n",
       "Anhui    2010    120.0                     36681.0   \n",
       "         2011    131.0                     36525.0   \n",
       "         2012    141.0                     37142.0   \n",
       "         2013    154.0                     37833.0   \n",
       "         2014    164.0                     38792.0   \n",
       "...                ...                         ...   \n",
       "Zhejiang 2019    366.0                     39341.0   \n",
       "         2020    406.0                     39750.0   \n",
       "         2021    425.0                     43838.0   \n",
       "         2022    432.0                     44092.0   \n",
       "         2023    206.0                     43467.0   \n",
       "\n",
       "variable       Performing arts groups (number)  \\\n",
       "region   year                                    \n",
       "Anhui    2010                           1255.0   \n",
       "         2011                           1294.0   \n",
       "         2012                           1015.0   \n",
       "         2013                            991.0   \n",
       "         2014                            988.0   \n",
       "...                                        ...   \n",
       "Zhejiang 2019                           1550.0   \n",
       "         2020                           1236.0   \n",
       "         2021                           1357.0   \n",
       "         2022                           1247.0   \n",
       "         2023                           1217.0   \n",
       "\n",
       "variable       Public toilets per 10000 people  \\\n",
       "region   year                                    \n",
       "Anhui    2010                          2.54531   \n",
       "         2011                          2.44509   \n",
       "         2012                          2.31572   \n",
       "         2013                          2.26129   \n",
       "         2014                          2.22846   \n",
       "...                                        ...   \n",
       "Zhejiang 2019                          3.01981   \n",
       "         2020                          3.05000   \n",
       "         2021                          2.98016   \n",
       "         2022                          3.13469   \n",
       "         2023                          2.89298   \n",
       "\n",
       "variable       Railway operating mileage (km)  Urban road lighting (thousands)  \n",
       "region   year                                                                   \n",
       "Anhui    2010                        2900.000                          548.635  \n",
       "         2011                        3100.000                          659.485  \n",
       "         2012                        3300.000                          722.017  \n",
       "         2013                        3500.000                          766.222  \n",
       "         2014                        3548.443                          809.764  \n",
       "...                                       ...                              ...  \n",
       "Zhejiang 2019                        2842.157                         1709.115  \n",
       "         2020                        3159.000                         1831.700  \n",
       "         2021                        3345.187                         1890.000  \n",
       "         2022                        3711.532                         1982.612  \n",
       "         2023                        3852.700                         2000.926  \n",
       "\n",
       "[430 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_region=df.drop('nationwide').dropna()\n",
    "df_region"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c81a820c-7f57-4cd8-8d17-8206d9aaade6",
   "metadata": {},
   "source": [
    "### Regression "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f410bb3-dcce-4238-b1ba-0d37089abaf3",
   "metadata": {},
   "source": [
    "  #### The Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8bafbc9-3262-4d87-b739-c384a06d84ab",
   "metadata": {},
   "source": [
    "We define \"total number of tourists\" as the dependent variable,Y. And the others are independent variables, X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d96b500-b872-4a1b-af88-ca689dc182c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>variable</th>\n",
       "      <th>Number of rooms in star rated hotels (rooms/set)</th>\n",
       "      <th>Number of star rated hotels (number)</th>\n",
       "      <th>Total number of tourist attractions (units)</th>\n",
       "      <th>Cleaning area (10000 square meters)</th>\n",
       "      <th>Consumer price index (last year=100)</th>\n",
       "      <th>Highway mileage (km)</th>\n",
       "      <th>Museums</th>\n",
       "      <th>Number of taxis (vehicles)</th>\n",
       "      <th>Performing arts groups (number)</th>\n",
       "      <th>Public toilets per 10000 people</th>\n",
       "      <th>Railway operating mileage (km)</th>\n",
       "      <th>Urban road lighting (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44265.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>17339.00</td>\n",
       "      <td>103.13920</td>\n",
       "      <td>149382.000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>1255.0</td>\n",
       "      <td>2.54531</td>\n",
       "      <td>2900.000</td>\n",
       "      <td>548.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43479.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>18315.00</td>\n",
       "      <td>105.56246</td>\n",
       "      <td>149535.000</td>\n",
       "      <td>131.0</td>\n",
       "      <td>36525.0</td>\n",
       "      <td>1294.0</td>\n",
       "      <td>2.44509</td>\n",
       "      <td>3100.000</td>\n",
       "      <td>659.485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44796.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>22229.00</td>\n",
       "      <td>102.26080</td>\n",
       "      <td>165157.000</td>\n",
       "      <td>141.0</td>\n",
       "      <td>37142.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>2.31572</td>\n",
       "      <td>3300.000</td>\n",
       "      <td>722.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45279.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>24081.00</td>\n",
       "      <td>102.40924</td>\n",
       "      <td>173763.000</td>\n",
       "      <td>154.0</td>\n",
       "      <td>37833.0</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2.26129</td>\n",
       "      <td>3500.000</td>\n",
       "      <td>766.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46328.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>26370.00</td>\n",
       "      <td>101.60000</td>\n",
       "      <td>174373.000</td>\n",
       "      <td>164.0</td>\n",
       "      <td>38792.0</td>\n",
       "      <td>988.0</td>\n",
       "      <td>2.22846</td>\n",
       "      <td>3548.443</td>\n",
       "      <td>809.764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>89507.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>798.0</td>\n",
       "      <td>47185.56</td>\n",
       "      <td>102.90000</td>\n",
       "      <td>121812.898</td>\n",
       "      <td>366.0</td>\n",
       "      <td>39341.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>3.01981</td>\n",
       "      <td>2842.157</td>\n",
       "      <td>1709.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>84286.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>827.0</td>\n",
       "      <td>53870.00</td>\n",
       "      <td>102.30000</td>\n",
       "      <td>123080.000</td>\n",
       "      <td>406.0</td>\n",
       "      <td>39750.0</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>3.05000</td>\n",
       "      <td>3159.000</td>\n",
       "      <td>1831.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>76545.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>909.0</td>\n",
       "      <td>57716.68</td>\n",
       "      <td>101.50000</td>\n",
       "      <td>123885.350</td>\n",
       "      <td>425.0</td>\n",
       "      <td>43838.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>2.98016</td>\n",
       "      <td>3345.187</td>\n",
       "      <td>1890.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>73510.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>929.0</td>\n",
       "      <td>58712.35</td>\n",
       "      <td>102.20000</td>\n",
       "      <td>122917.737</td>\n",
       "      <td>432.0</td>\n",
       "      <td>44092.0</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>3.13469</td>\n",
       "      <td>3711.532</td>\n",
       "      <td>1982.612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>68079.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>947.0</td>\n",
       "      <td>64727.88</td>\n",
       "      <td>100.30000</td>\n",
       "      <td>121408.000</td>\n",
       "      <td>206.0</td>\n",
       "      <td>43467.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>2.89298</td>\n",
       "      <td>3852.700</td>\n",
       "      <td>2000.926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>430 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "variable  Number of rooms in star rated hotels (rooms/set)  \\\n",
       "0                                                  44265.0   \n",
       "1                                                  43479.0   \n",
       "2                                                  44796.0   \n",
       "3                                                  45279.0   \n",
       "4                                                  46328.0   \n",
       "..                                                     ...   \n",
       "425                                                89507.0   \n",
       "426                                                84286.0   \n",
       "427                                                76545.0   \n",
       "428                                                73510.0   \n",
       "429                                                68079.0   \n",
       "\n",
       "variable  Number of star rated hotels (number)  \\\n",
       "0                                        417.0   \n",
       "1                                        390.0   \n",
       "2                                        383.0   \n",
       "3                                        377.0   \n",
       "4                                        367.0   \n",
       "..                                         ...   \n",
       "425                                      528.0   \n",
       "426                                      500.0   \n",
       "427                                      453.0   \n",
       "428                                      437.0   \n",
       "429                                      410.0   \n",
       "\n",
       "variable  Total number of tourist attractions (units)  \\\n",
       "0                                               276.0   \n",
       "1                                               377.0   \n",
       "2                                               397.0   \n",
       "3                                               444.0   \n",
       "4                                               483.0   \n",
       "..                                                ...   \n",
       "425                                             798.0   \n",
       "426                                             827.0   \n",
       "427                                             909.0   \n",
       "428                                             929.0   \n",
       "429                                             947.0   \n",
       "\n",
       "variable  Cleaning area (10000 square meters)  \\\n",
       "0                                    17339.00   \n",
       "1                                    18315.00   \n",
       "2                                    22229.00   \n",
       "3                                    24081.00   \n",
       "4                                    26370.00   \n",
       "..                                        ...   \n",
       "425                                  47185.56   \n",
       "426                                  53870.00   \n",
       "427                                  57716.68   \n",
       "428                                  58712.35   \n",
       "429                                  64727.88   \n",
       "\n",
       "variable  Consumer price index (last year=100)  Highway mileage (km)  Museums  \\\n",
       "0                                    103.13920            149382.000    120.0   \n",
       "1                                    105.56246            149535.000    131.0   \n",
       "2                                    102.26080            165157.000    141.0   \n",
       "3                                    102.40924            173763.000    154.0   \n",
       "4                                    101.60000            174373.000    164.0   \n",
       "..                                         ...                   ...      ...   \n",
       "425                                  102.90000            121812.898    366.0   \n",
       "426                                  102.30000            123080.000    406.0   \n",
       "427                                  101.50000            123885.350    425.0   \n",
       "428                                  102.20000            122917.737    432.0   \n",
       "429                                  100.30000            121408.000    206.0   \n",
       "\n",
       "variable  Number of taxis (vehicles)  Performing arts groups (number)  \\\n",
       "0                            36681.0                           1255.0   \n",
       "1                            36525.0                           1294.0   \n",
       "2                            37142.0                           1015.0   \n",
       "3                            37833.0                            991.0   \n",
       "4                            38792.0                            988.0   \n",
       "..                               ...                              ...   \n",
       "425                          39341.0                           1550.0   \n",
       "426                          39750.0                           1236.0   \n",
       "427                          43838.0                           1357.0   \n",
       "428                          44092.0                           1247.0   \n",
       "429                          43467.0                           1217.0   \n",
       "\n",
       "variable  Public toilets per 10000 people  Railway operating mileage (km)  \\\n",
       "0                                 2.54531                        2900.000   \n",
       "1                                 2.44509                        3100.000   \n",
       "2                                 2.31572                        3300.000   \n",
       "3                                 2.26129                        3500.000   \n",
       "4                                 2.22846                        3548.443   \n",
       "..                                    ...                             ...   \n",
       "425                               3.01981                        2842.157   \n",
       "426                               3.05000                        3159.000   \n",
       "427                               2.98016                        3345.187   \n",
       "428                               3.13469                        3711.532   \n",
       "429                               2.89298                        3852.700   \n",
       "\n",
       "variable  Urban road lighting (thousands)  \n",
       "0                                 548.635  \n",
       "1                                 659.485  \n",
       "2                                 722.017  \n",
       "3                                 766.222  \n",
       "4                                 809.764  \n",
       "..                                    ...  \n",
       "425                              1709.115  \n",
       "426                              1831.700  \n",
       "427                              1890.000  \n",
       "428                              1982.612  \n",
       "429                              2000.926  \n",
       "\n",
       "[430 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df_region.reset_index().drop(['Total number of tourists (100 million)','year','region'],axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "432579c1-19ba-4eb7-adc7-62ffb595727a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.71475\n",
       "1      1.06000\n",
       "2      1.80000\n",
       "3      1.80000\n",
       "4      1.76000\n",
       "        ...   \n",
       "425    5.12309\n",
       "426    2.95000\n",
       "427    3.24000\n",
       "428    2.42000\n",
       "429    5.23000\n",
       "Name: Total number of tourists (100 million), Length: 430, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df_region.reset_index()['Total number of tourists (100 million)']\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1edadc3e-b967-4f86-984f-5e2d6c240170",
   "metadata": {},
   "source": [
    "#### Linear Regression with one regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c36642be-93d7-40c0-8177-fb97c3541ed1",
   "metadata": {},
   "source": [
    "Since the total number of tourist attractions may be the main factor that influences the total number of tourists, We start with linear regression taking the total number of tourist attractions as the only regressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4503d1af-9758-4102-a601-7095b21ebd63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit model: tourists = 0.17444784622341136 + 0.0033496039337555846 tourist_attractions\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "tourist_attractions_lr_model = linear_model.LinearRegression()\n",
    "tourist_attractions_lr_model.fit(X[[\"Total number of tourist attractions (units)\"]], Y) \n",
    "beta_0 = tourist_attractions_lr_model.intercept_\n",
    "beta_1 = tourist_attractions_lr_model.coef_[0] \n",
    "\n",
    "print(f\"Fit model: tourists = {beta_0} + {beta_1} tourist_attractions\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b67888-a07f-4c2b-9a6a-b7915b1f3f6d",
   "metadata": {},
   "source": [
    "It implies that an increase in the tourist attraction increases the tourists by about 0.335 million per year."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "125dddb8-1031-4577-bbc1-960807bce97d",
   "metadata": {},
   "source": [
    "#### Multiple Linear Regression "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92ffd191-aa20-4396-a226-e136cd281400",
   "metadata": {},
   "source": [
    "In this part, we use all of the variables in X as the regressors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "72e5d83b-9fd9-4269-88bf-e4d52e3d44c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy_X&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">1e-06</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">positive&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_model = linear_model.LinearRegression()\n",
    "mlr_model.fit(X, Y) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3779f469-f669-45dc-accf-720ad8f1d7c0",
   "metadata": {},
   "source": [
    "The predicted coefficients of the regressors are as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f69de3fa-e831-4615-b12f-aae4c0bc5c20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Number of rooms in star rated hotels (rooms/set)    1.508047e-05\n",
       "Number of star rated hotels (number)               -5.798210e-04\n",
       "Total number of tourist attractions (units)         2.391866e-03\n",
       "Cleaning area (10000 square meters)                -1.669221e-05\n",
       "Consumer price index (last year=100)               -2.314084e-02\n",
       "Highway mileage (km)                                8.945709e-07\n",
       "Museums                                             4.513309e-04\n",
       "Number of taxis (vehicles)                         -5.327217e-06\n",
       "Performing arts groups (number)                     9.023723e-05\n",
       "Public toilets per 10000 people                    -4.266502e-03\n",
       "Railway operating mileage (km)                     -9.809363e-05\n",
       "Urban road lighting (thousands)                     8.692668e-04\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_coefs = pd.Series(dict(zip(X.columns, mlr_model.coef_)))\n",
    "mlr_coefs "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ecc55f4-f93d-4a97-9b39-faf27c696ba6",
   "metadata": {},
   "source": [
    "Some coefficients are surprising because they imply that the directions of the independent variables' influences on the dependent variable are opposite to our intuition. To examine the accuracy of the  prediction, We compute RMSE of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e7d807df-96e9-4eb8-a642-490253f47b5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5874968695996544"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "(metrics.mean_squared_error(Y, mlr_model.predict(X)))**(1/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e96eb5d-6272-4eaf-9936-f32d218ba697",
   "metadata": {},
   "source": [
    "This means the mistake of multiple linear regression model's prediction is about 59 million tourists. We want to improve our prediction, so next we add nonlinear variables into linear regression."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c612fb8a-7e5d-4874-b05b-f22e536aa570",
   "metadata": {},
   "source": [
    "#### Nonlinear Variables in Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d55c77eb-d5b0-4dae-b634-5043674f20d9",
   "metadata": {},
   "source": [
    "We first draw the scatter plots to identify the relationships between the dependent variable and the independent variables and to find a proper function form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8be958be-98a9-4f7f-a24f-ec505f782bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4,ncols=3,sharex=False,sharey=True,figsize=(15,10))\n",
    "for row in range(4):\n",
    "        for col in range(3):\n",
    "            axes[row,col].scatter(X.iloc[:,row*3+col], Y, s=1.5,alpha=0.5)\n",
    "            axes[row,col].set_xlabel(X.columns[row*3+col])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "633f20bb-5470-413b-bbba-fc988e029af4",
   "metadata": {},
   "source": [
    "According to the scatter plots, we make some nonlinear transformation on the independent variables and then fit the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "775b76f5-d3f9-4e70-b2f0-55a50087937f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5512984481454307"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_nl=X.copy()\n",
    "for i in X.drop(['Public toilets per 10000 people'],axis=1).columns:\n",
    "    X_nl[i+'_square']=X[i]**2\n",
    "X_nl['Public toilets per 10000 people_inverse']=X['Public toilets per 10000 people']**(-1)\n",
    "X_nl=X_nl.drop(['Public toilets per 10000 people'],axis=1)\n",
    "\n",
    "nlr_model = linear_model.LinearRegression()\n",
    "nlr_model.fit(X_nl, Y) \n",
    "\n",
    "(metrics.mean_squared_error(Y, nlr_model.predict(X_nl)))**(1/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c9017e4-3fa1-401d-896b-f1ad463c6280",
   "metadata": {},
   "source": [
    "The RMSE of the nonlinear model is a bit smaller than the linear model, implying that the accuracy of the prediction is improved."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8aab6fa-0233-427a-9354-826a12096f05",
   "metadata": {},
   "source": [
    "### Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1084dba9-9a33-4e18-a32d-a2b2664dc88c",
   "metadata": {},
   "source": [
    "We want to assess which medel makes the best prediction, so we divide the dataset into train set and test set and compute MSE of each set and each model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc409f4e-308d-482f-a303-50dbbf6ba327",
   "metadata": {},
   "source": [
    "We also want to include Lasso Regression, Random Forest and Neural Networks into the comparison. So before computing MSE, we first construct the above regressions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "146fcbb2-116a-4694-b3c4-d50bbb8af141",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lasso Regression\n",
    "from sklearn import preprocessing, pipeline\n",
    "from sklearn import neural_network\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import tree\n",
    "lasso = linear_model.LassoCV(cv=5)\n",
    "\n",
    "# Random Tree and Random Forests\n",
    "random_tree = tree.DecisionTreeRegressor(max_depth=5)\n",
    "random_forest = RandomForestRegressor(n_estimators=25)\n",
    "\n",
    "# Neural Networks\n",
    "neural_network_model = pipeline.make_pipeline(preprocessing.StandardScaler(), neural_network.MLPRegressor(hidden_layer_sizes=(30, 20), max_iter=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a9ca58b-d71a-4d7c-a564-70b4bba50058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse_train</th>\n",
       "      <th>mse_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tourist_attractions_lr_model</th>\n",
       "      <td>0.735540</td>\n",
       "      <td>0.645482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlr_model</th>\n",
       "      <td>0.362971</td>\n",
       "      <td>0.300949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nlr_model</th>\n",
       "      <td>0.305778</td>\n",
       "      <td>0.315496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lasso</th>\n",
       "      <td>0.802575</td>\n",
       "      <td>0.858008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_tree</th>\n",
       "      <td>0.168045</td>\n",
       "      <td>0.493124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_forest</th>\n",
       "      <td>0.048415</td>\n",
       "      <td>0.270492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neural_network</th>\n",
       "      <td>0.041521</td>\n",
       "      <td>0.354288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              mse_train  mse_test\n",
       "model                                            \n",
       "tourist_attractions_lr_model   0.735540  0.645482\n",
       "mlr_model                      0.362971  0.300949\n",
       "nlr_model                      0.305778  0.315496\n",
       "lasso                          0.802575  0.858008\n",
       "random_tree                    0.168045  0.493124\n",
       "random_forest                  0.048415  0.270492\n",
       "neural_network                 0.041521  0.354288"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "import numpy as np\n",
    "np.random.seed(42)\n",
    "\n",
    "#  linear regression with one regressor\n",
    "model_lr = [tourist_attractions_lr_model]\n",
    "str_model_lr = ['tourist_attractions_lr_model']\n",
    "\n",
    "# multiple linear regression\n",
    "model_mlr = [mlr_model]\n",
    "str_model_mlr = ['mlr_model']\n",
    "\n",
    "# nonlinear regression\n",
    "model_nl = [nlr_model, lasso, random_tree, random_forest, neural_network_model]\n",
    "str_model_nl = ['nlr_model', 'lasso', 'random_tree', 'random_forest', 'neural_network']\n",
    "\n",
    "\n",
    "def model_mse(mod, str_mod, x, y):\n",
    "    result = []\n",
    "    x_train, x_test, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "    for j in range(len(mod)):\n",
    "        i = mod[j]\n",
    "        k = str_mod[j]\n",
    "        i.fit(x_train, y_train)\n",
    "        mse_train = metrics.mean_squared_error(y_train, i.predict(x_train))\n",
    "        mse_test = metrics.mean_squared_error(y_test, i.predict(x_test))\n",
    "        result.append([k, mse_train, mse_test])\n",
    "    return result\n",
    "\n",
    "\n",
    "lr_mse = model_mse(model_lr, str_model_lr, X[[\"Total number of tourist attractions (units)\"]], Y)\n",
    "mlr_mse = model_mse(model_mlr, str_model_mlr, X, Y)\n",
    "nl_mse = model_mse(model_nl, str_model_nl, X_nl, Y)\n",
    "\n",
    "mse_list = lr_mse + mlr_mse + nl_mse\n",
    "mse_df = pd.DataFrame(mse_list, columns=['model', 'mse_train', 'mse_test']).set_index(['model'])\n",
    "mse_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f9c9542-20da-442d-9358-dc273d0e84f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'random_forest'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_df['mse_test'].idxmin()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d7ca8ca-a9df-40d9-a76f-072b61ac5b73",
   "metadata": {},
   "source": [
    "It implies that Random Forest makes the best prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f4a4d8c-5b38-4379-8b9d-286db50d81af",
   "metadata": {},
   "source": [
    "## Seeking obscure but worth visiting attractions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff8b234-d861-442b-99e4-f95d8e049751",
   "metadata": {},
   "source": [
    "We want find some obscure but worth visiting tourist attractions. Thus, we collect data on the number of A-level scenic spots in each provincial-level administrative region from 2018 to 2023. The data comes from China Culture Relics and Tourism Statistical Yearbook, collated by CEI data.\n",
    "\n",
    "We construct a new variable, score tourists ratio(STR),which is scenic spots's score divided by Total number of tourists. Scenic spots's score is the weighted average of the number of A-level scenic spots. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ebdf3b4-fd38-431c-b6d7-30e413ad7481",
   "metadata": {},
   "source": [
    "### Clean the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bc2e9a43-c204-469c-a3a6-313a252b48cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>STR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Beijing</th>\n",
       "      <td>2023</td>\n",
       "      <td>398.295455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beijing</th>\n",
       "      <td>2022</td>\n",
       "      <td>843.373494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beijing</th>\n",
       "      <td>2021</td>\n",
       "      <td>647.747748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beijing</th>\n",
       "      <td>2020</td>\n",
       "      <td>773.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beijing</th>\n",
       "      <td>2019</td>\n",
       "      <td>289.726129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xinjiang</th>\n",
       "      <td>2022</td>\n",
       "      <td>4558.695652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xinjiang</th>\n",
       "      <td>2021</td>\n",
       "      <td>3237.096774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xinjiang</th>\n",
       "      <td>2020</td>\n",
       "      <td>2941.509434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xinjiang</th>\n",
       "      <td>2019</td>\n",
       "      <td>1397.022769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xinjiang</th>\n",
       "      <td>2018</td>\n",
       "      <td>1668.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          year          STR\n",
       "region                     \n",
       "Beijing   2023   398.295455\n",
       "Beijing   2022   843.373494\n",
       "Beijing   2021   647.747748\n",
       "Beijing   2020   773.684211\n",
       "Beijing   2019   289.726129\n",
       "...        ...          ...\n",
       "Xinjiang  2022  4558.695652\n",
       "Xinjiang  2021  3237.096774\n",
       "Xinjiang  2020  2941.509434\n",
       "Xinjiang  2019  1397.022769\n",
       "Xinjiang  2018  1668.750000\n",
       "\n",
       "[186 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def read(i):\n",
    "        j=pd.read_excel(\"A_scenic_spots.xlsx\", str(i))\n",
    "        j['year']=int(i) # add a new column:year\n",
    "        j=j.set_index(['region','year']).drop(['total'],axis=1).drop(['nationwide'],level=0).fillna(0)\n",
    "        for col in j.columns:\n",
    "            j[col] = j[col].astype(int)\n",
    "        j['score']=j['5A']*5+j['4A']*4+j['3A']*3+j['2A']*2+j['1A']*1\n",
    "        j=j.drop(['5A','4A','3A','2A','1A'],axis=1)\n",
    "        return j\n",
    "\n",
    "A=pd.concat([read(2023),read(2022),read(2021),read(2020),read(2019),read(2018)],axis=0,join='outer')\n",
    "A=pd.merge(A,df_region[['Total number of tourists (100 million)']],on=['region','year'],how='left')\n",
    "A['STR']=A['score']/A['Total number of tourists (100 million)']\n",
    "A=A.drop(['Total number of tourists (100 million)','score'],axis=1)\n",
    "region=pd.read_excel(\"A_scenic_spots.xlsx\", '2022').set_index(['region']).drop(['nationwide']).index\n",
    "\n",
    "A_region=[]\n",
    "for i in region:\n",
    "    j=A.groupby('region').get_group(i)\n",
    "    A_region.append(j)\n",
    "merged_A = pd.concat(A_region, axis=0, ignore_index=False).reset_index(level='year')\n",
    "merged_A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69587dad-512b-4e3d-bfed-6b6da2fb21a6",
   "metadata": {},
   "source": [
    "We use the weighted average of STR for each year to make comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "64ef6ae9-03f1-4482-b595-d2049abf54a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute weighted average\n",
    "merged_A['weight']=np.exp(merged_A['year'] - 2018)\n",
    "merged_A['normal_weight']=merged_A['weight'] / merged_A.groupby('region')['weight'].transform('sum')\n",
    "merged_A['weighted_STR']=merged_A['normal_weight']*merged_A['STR']\n",
    "final_A=merged_A.groupby('region')['weighted_STR'].sum().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9ef6a0-72c7-4c1f-926b-2648f7ad4e5b",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2523eea3-d812-499a-882f-19017e130ce9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gb</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>region</th>\n",
       "      <th>weighted_STR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>安徽省</td>\n",
       "      <td>156340000</td>\n",
       "      <td>MULTIPOLYGON (((116.42396 34.63194, 116.42853 ...</td>\n",
       "      <td>32</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>1407.017715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>北京市</td>\n",
       "      <td>156110000</td>\n",
       "      <td>MULTIPOLYGON (((116.66689 40.97671, 116.68126 ...</td>\n",
       "      <td>14</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>533.668809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>境界线</td>\n",
       "      <td></td>\n",
       "      <td>MULTILINESTRING ((113.82228 22.22917, 113.8171...</td>\n",
       "      <td>2</td>\n",
       "      <td>Boundary line</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>境界线</td>\n",
       "      <td></td>\n",
       "      <td>MULTILINESTRING ((121.33136 31.5059, 121.3329 ...</td>\n",
       "      <td>9</td>\n",
       "      <td>Boundary line</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>境界线</td>\n",
       "      <td></td>\n",
       "      <td>MULTILINESTRING ((73.88145 38.57873, 73.89119 ...</td>\n",
       "      <td>12</td>\n",
       "      <td>Boundary line</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>境界线</td>\n",
       "      <td></td>\n",
       "      <td>MULTILINESTRING ((121.2676 30.70163, 121.26914...</td>\n",
       "      <td>17</td>\n",
       "      <td>Boundary line</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>境界线</td>\n",
       "      <td></td>\n",
       "      <td>MULTILINESTRING ((109.81908 20.11682, 109.8251...</td>\n",
       "      <td>21</td>\n",
       "      <td>Boundary line</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>境界线</td>\n",
       "      <td></td>\n",
       "      <td>MULTILINESTRING ((117.18804 23.61724, 117.1880...</td>\n",
       "      <td>29</td>\n",
       "      <td>Boundary line</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>境界线</td>\n",
       "      <td></td>\n",
       "      <td>MULTILINESTRING ((108.30202 6.01089, 108.29262...</td>\n",
       "      <td>35</td>\n",
       "      <td>Boundary line</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>境界线</td>\n",
       "      <td></td>\n",
       "      <td>MULTILINESTRING ((120.5063 27.0886, 120.46489 ...</td>\n",
       "      <td>40</td>\n",
       "      <td>Boundary line</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>重庆市</td>\n",
       "      <td>156500000</td>\n",
       "      <td>MULTIPOLYGON (((108.53555 32.19219, 108.58077 ...</td>\n",
       "      <td>18</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>679.918884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>福建省</td>\n",
       "      <td>156350000</td>\n",
       "      <td>MULTIPOLYGON (((117.41987 23.76582, 117.4411 2...</td>\n",
       "      <td>22</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>1103.737494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>甘肃省</td>\n",
       "      <td>156620000</td>\n",
       "      <td>MULTIPOLYGON (((97.17195 42.79342, 97.84515 41...</td>\n",
       "      <td>5</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>2073.715863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>广东省</td>\n",
       "      <td>156440000</td>\n",
       "      <td>MULTIPOLYGON (((110.36308 20.69757, 110.36827 ...</td>\n",
       "      <td>23</td>\n",
       "      <td>Guangdong</td>\n",
       "      <td>831.095222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>广西壮族自治区</td>\n",
       "      <td>156450000</td>\n",
       "      <td>MULTIPOLYGON (((109.13892 21.04783, 109.12366 ...</td>\n",
       "      <td>10</td>\n",
       "      <td>Guangxi</td>\n",
       "      <td>1319.504229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>贵州省</td>\n",
       "      <td>156520000</td>\n",
       "      <td>MULTIPOLYGON (((109.57999 26.75606, 109.57092 ...</td>\n",
       "      <td>41</td>\n",
       "      <td>Guizhou</td>\n",
       "      <td>1487.016583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>海南省</td>\n",
       "      <td>156460000</td>\n",
       "      <td>MULTIPOLYGON (((112.05623 3.83753, 112.04267 3...</td>\n",
       "      <td>3</td>\n",
       "      <td>Hainan</td>\n",
       "      <td>828.887715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>河北省</td>\n",
       "      <td>156130000</td>\n",
       "      <td>MULTIPOLYGON (((118.61425 39, 118.59575 38.974...</td>\n",
       "      <td>38</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>2304.575694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>黑龙江省</td>\n",
       "      <td>156230000</td>\n",
       "      <td>MULTIPOLYGON (((133.86551 46.50956, 133.86486 ...</td>\n",
       "      <td>27</td>\n",
       "      <td>Heilongjiang</td>\n",
       "      <td>3899.697948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>河南省</td>\n",
       "      <td>156410000</td>\n",
       "      <td>MULTIPOLYGON (((111.02261 33.17939, 111.00594 ...</td>\n",
       "      <td>0</td>\n",
       "      <td>Henan</td>\n",
       "      <td>1583.398206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>香港特别行政区</td>\n",
       "      <td>156810000</td>\n",
       "      <td>MULTIPOLYGON (((114.27453 22.16333, 114.25354 ...</td>\n",
       "      <td>26</td>\n",
       "      <td>Hong Kong Special Administrative Region</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>湖北省</td>\n",
       "      <td>156420000</td>\n",
       "      <td>MULTIPOLYGON (((113.11633 29.42347, 113.11209 ...</td>\n",
       "      <td>6</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>1362.480957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>湖南省</td>\n",
       "      <td>156430000</td>\n",
       "      <td>MULTIPOLYGON (((109.47756 26.83885, 109.47058 ...</td>\n",
       "      <td>36</td>\n",
       "      <td>Hunan</td>\n",
       "      <td>910.696903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>内蒙古自治区</td>\n",
       "      <td>156150000</td>\n",
       "      <td>MULTIPOLYGON (((121.48938 53.33117, 121.50035 ...</td>\n",
       "      <td>8</td>\n",
       "      <td>Inner_Mongolia</td>\n",
       "      <td>3943.945326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>江苏省</td>\n",
       "      <td>156320000</td>\n",
       "      <td>MULTIPOLYGON (((119.36827 34.76344, 119.44021 ...</td>\n",
       "      <td>11</td>\n",
       "      <td>Jiangsu</td>\n",
       "      <td>496.504163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>江西省</td>\n",
       "      <td>156360000</td>\n",
       "      <td>MULTIPOLYGON (((116.77847 30.0376, 116.79547 3...</td>\n",
       "      <td>25</td>\n",
       "      <td>Jiangxi</td>\n",
       "      <td>745.559762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>吉林省</td>\n",
       "      <td>156220000</td>\n",
       "      <td>MULTIPOLYGON (((123.89639 46.29755, 123.89848 ...</td>\n",
       "      <td>20</td>\n",
       "      <td>Jilin</td>\n",
       "      <td>2748.268136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>辽宁省</td>\n",
       "      <td>156210000</td>\n",
       "      <td>MULTIPOLYGON (((122.74553 39.03193, 122.75363 ...</td>\n",
       "      <td>34</td>\n",
       "      <td>Liaoning</td>\n",
       "      <td>1457.006235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>澳门特别行政区</td>\n",
       "      <td>156820000</td>\n",
       "      <td>MULTIPOLYGON (((113.57004 22.11853, 113.55014 ...</td>\n",
       "      <td>19</td>\n",
       "      <td>Macao Special Administrative Region</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>宁夏回族自治区</td>\n",
       "      <td>156640000</td>\n",
       "      <td>MULTIPOLYGON (((106.0657 35.4375, 106.06508 35...</td>\n",
       "      <td>33</td>\n",
       "      <td>Ningxia</td>\n",
       "      <td>2014.330610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>青海省</td>\n",
       "      <td>156630000</td>\n",
       "      <td>MULTIPOLYGON (((97.0967 39.19702, 97.12546 39....</td>\n",
       "      <td>16</td>\n",
       "      <td>Qinghai</td>\n",
       "      <td>2088.826918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>陕西省</td>\n",
       "      <td>156610000</td>\n",
       "      <td>MULTIPOLYGON (((111.14708 39.56757, 111.14179 ...</td>\n",
       "      <td>37</td>\n",
       "      <td>Shaanxi</td>\n",
       "      <td>1334.061343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>山东省</td>\n",
       "      <td>156370000</td>\n",
       "      <td>MULTIPOLYGON (((119.89227 35.01009, 119.89441 ...</td>\n",
       "      <td>13</td>\n",
       "      <td>Shandong</td>\n",
       "      <td>1275.913246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>上海市</td>\n",
       "      <td>156310000</td>\n",
       "      <td>MULTIPOLYGON (((121.95683 31.23136, 122.00238 ...</td>\n",
       "      <td>24</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>485.748811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>山西省</td>\n",
       "      <td>156140000</td>\n",
       "      <td>MULTIPOLYGON (((114.12488 40.74365, 114.13835 ...</td>\n",
       "      <td>28</td>\n",
       "      <td>Shanxi</td>\n",
       "      <td>1808.525601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>四川省</td>\n",
       "      <td>156510000</td>\n",
       "      <td>MULTIPOLYGON (((102.94759 34.29356, 102.9564 3...</td>\n",
       "      <td>31</td>\n",
       "      <td>Sichuan</td>\n",
       "      <td>952.455026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>台湾省</td>\n",
       "      <td>156710000</td>\n",
       "      <td>MULTIPOLYGON (((121.60056 22.02062, 121.59325 ...</td>\n",
       "      <td>4</td>\n",
       "      <td>Taiwan Province</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>天津市</td>\n",
       "      <td>156120000</td>\n",
       "      <td>MULTIPOLYGON (((117.46737 40.23886, 117.49701 ...</td>\n",
       "      <td>7</td>\n",
       "      <td>Tianjin</td>\n",
       "      <td>524.412805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>西藏自治区</td>\n",
       "      <td>156540000</td>\n",
       "      <td>MULTIPOLYGON (((86.22214 28.09623, 86.22079 28...</td>\n",
       "      <td>15</td>\n",
       "      <td>Tibet</td>\n",
       "      <td>11629.104184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>156650000</td>\n",
       "      <td>MULTIPOLYGON (((78.05405 35.40122, 78.05606 35...</td>\n",
       "      <td>39</td>\n",
       "      <td>Xinjiang</td>\n",
       "      <td>2822.583766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>云南省</td>\n",
       "      <td>156530000</td>\n",
       "      <td>MULTIPOLYGON (((99.11815 29.19243, 99.10934 29...</td>\n",
       "      <td>30</td>\n",
       "      <td>Yunnan</td>\n",
       "      <td>1453.913774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>浙江省</td>\n",
       "      <td>156330000</td>\n",
       "      <td>MULTIPOLYGON (((121.08199 27.47084, 121.09719 ...</td>\n",
       "      <td>1</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>762.499270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name         gb                                           geometry  \\\n",
       "0        安徽省  156340000  MULTIPOLYGON (((116.42396 34.63194, 116.42853 ...   \n",
       "1        北京市  156110000  MULTIPOLYGON (((116.66689 40.97671, 116.68126 ...   \n",
       "2        境界线             MULTILINESTRING ((113.82228 22.22917, 113.8171...   \n",
       "3        境界线             MULTILINESTRING ((121.33136 31.5059, 121.3329 ...   \n",
       "4        境界线             MULTILINESTRING ((73.88145 38.57873, 73.89119 ...   \n",
       "5        境界线             MULTILINESTRING ((121.2676 30.70163, 121.26914...   \n",
       "6        境界线             MULTILINESTRING ((109.81908 20.11682, 109.8251...   \n",
       "7        境界线             MULTILINESTRING ((117.18804 23.61724, 117.1880...   \n",
       "8        境界线             MULTILINESTRING ((108.30202 6.01089, 108.29262...   \n",
       "9        境界线             MULTILINESTRING ((120.5063 27.0886, 120.46489 ...   \n",
       "10       重庆市  156500000  MULTIPOLYGON (((108.53555 32.19219, 108.58077 ...   \n",
       "11       福建省  156350000  MULTIPOLYGON (((117.41987 23.76582, 117.4411 2...   \n",
       "12       甘肃省  156620000  MULTIPOLYGON (((97.17195 42.79342, 97.84515 41...   \n",
       "13       广东省  156440000  MULTIPOLYGON (((110.36308 20.69757, 110.36827 ...   \n",
       "14   广西壮族自治区  156450000  MULTIPOLYGON (((109.13892 21.04783, 109.12366 ...   \n",
       "15       贵州省  156520000  MULTIPOLYGON (((109.57999 26.75606, 109.57092 ...   \n",
       "16       海南省  156460000  MULTIPOLYGON (((112.05623 3.83753, 112.04267 3...   \n",
       "17       河北省  156130000  MULTIPOLYGON (((118.61425 39, 118.59575 38.974...   \n",
       "18      黑龙江省  156230000  MULTIPOLYGON (((133.86551 46.50956, 133.86486 ...   \n",
       "19       河南省  156410000  MULTIPOLYGON (((111.02261 33.17939, 111.00594 ...   \n",
       "20   香港特别行政区  156810000  MULTIPOLYGON (((114.27453 22.16333, 114.25354 ...   \n",
       "21       湖北省  156420000  MULTIPOLYGON (((113.11633 29.42347, 113.11209 ...   \n",
       "22       湖南省  156430000  MULTIPOLYGON (((109.47756 26.83885, 109.47058 ...   \n",
       "23    内蒙古自治区  156150000  MULTIPOLYGON (((121.48938 53.33117, 121.50035 ...   \n",
       "24       江苏省  156320000  MULTIPOLYGON (((119.36827 34.76344, 119.44021 ...   \n",
       "25       江西省  156360000  MULTIPOLYGON (((116.77847 30.0376, 116.79547 3...   \n",
       "26       吉林省  156220000  MULTIPOLYGON (((123.89639 46.29755, 123.89848 ...   \n",
       "27       辽宁省  156210000  MULTIPOLYGON (((122.74553 39.03193, 122.75363 ...   \n",
       "28   澳门特别行政区  156820000  MULTIPOLYGON (((113.57004 22.11853, 113.55014 ...   \n",
       "29   宁夏回族自治区  156640000  MULTIPOLYGON (((106.0657 35.4375, 106.06508 35...   \n",
       "30       青海省  156630000  MULTIPOLYGON (((97.0967 39.19702, 97.12546 39....   \n",
       "31       陕西省  156610000  MULTIPOLYGON (((111.14708 39.56757, 111.14179 ...   \n",
       "32       山东省  156370000  MULTIPOLYGON (((119.89227 35.01009, 119.89441 ...   \n",
       "33       上海市  156310000  MULTIPOLYGON (((121.95683 31.23136, 122.00238 ...   \n",
       "34       山西省  156140000  MULTIPOLYGON (((114.12488 40.74365, 114.13835 ...   \n",
       "35       四川省  156510000  MULTIPOLYGON (((102.94759 34.29356, 102.9564 3...   \n",
       "36       台湾省  156710000  MULTIPOLYGON (((121.60056 22.02062, 121.59325 ...   \n",
       "37       天津市  156120000  MULTIPOLYGON (((117.46737 40.23886, 117.49701 ...   \n",
       "38     西藏自治区  156540000  MULTIPOLYGON (((86.22214 28.09623, 86.22079 28...   \n",
       "39  新疆维吾尔自治区  156650000  MULTIPOLYGON (((78.05405 35.40122, 78.05606 35...   \n",
       "40       云南省  156530000  MULTIPOLYGON (((99.11815 29.19243, 99.10934 29...   \n",
       "41       浙江省  156330000  MULTIPOLYGON (((121.08199 27.47084, 121.09719 ...   \n",
       "\n",
       "    Unnamed: 0                                   region  weighted_STR  \n",
       "0           32                                    Anhui   1407.017715  \n",
       "1           14                                  Beijing    533.668809  \n",
       "2            2                            Boundary line      0.000000  \n",
       "3            9                            Boundary line      0.000000  \n",
       "4           12                            Boundary line      0.000000  \n",
       "5           17                            Boundary line      0.000000  \n",
       "6           21                            Boundary line      0.000000  \n",
       "7           29                            Boundary line      0.000000  \n",
       "8           35                            Boundary line      0.000000  \n",
       "9           40                            Boundary line      0.000000  \n",
       "10          18                                Chongqing    679.918884  \n",
       "11          22                                   Fujian   1103.737494  \n",
       "12           5                                    Gansu   2073.715863  \n",
       "13          23                                Guangdong    831.095222  \n",
       "14          10                                  Guangxi   1319.504229  \n",
       "15          41                                  Guizhou   1487.016583  \n",
       "16           3                                   Hainan    828.887715  \n",
       "17          38                                    Hebei   2304.575694  \n",
       "18          27                             Heilongjiang   3899.697948  \n",
       "19           0                                    Henan   1583.398206  \n",
       "20          26  Hong Kong Special Administrative Region      0.000000  \n",
       "21           6                                    Hubei   1362.480957  \n",
       "22          36                                    Hunan    910.696903  \n",
       "23           8                           Inner_Mongolia   3943.945326  \n",
       "24          11                                  Jiangsu    496.504163  \n",
       "25          25                                  Jiangxi    745.559762  \n",
       "26          20                                    Jilin   2748.268136  \n",
       "27          34                                 Liaoning   1457.006235  \n",
       "28          19      Macao Special Administrative Region      0.000000  \n",
       "29          33                                  Ningxia   2014.330610  \n",
       "30          16                                  Qinghai   2088.826918  \n",
       "31          37                                  Shaanxi   1334.061343  \n",
       "32          13                                 Shandong   1275.913246  \n",
       "33          24                                 Shanghai    485.748811  \n",
       "34          28                                   Shanxi   1808.525601  \n",
       "35          31                                  Sichuan    952.455026  \n",
       "36           4                          Taiwan Province      0.000000  \n",
       "37           7                                  Tianjin    524.412805  \n",
       "38          15                                    Tibet  11629.104184  \n",
       "39          39                                 Xinjiang   2822.583766  \n",
       "40          30                                   Yunnan   1453.913774  \n",
       "41           1                                 Zhejiang    762.499270  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "province= gpd.read_file('province_map.geojson')\n",
    "translation_region=pd.read_excel(\"translation_region.xlsx\", \"Sheet2\")\n",
    "geo_tran=pd.merge(province,translation_region,left_index=True,right_index=True)\n",
    "geo_merge=pd.merge(geo_tran,final_A,on='region',how='outer')\n",
    "geo_merge['weighted_STR'] = geo_merge['weighted_STR'].fillna(0)  \n",
    "geo_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a186a6e3-6fed-4095-a10e-9b681b9fd1a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R5qNNEgCePbif/PnK8w8pXLrCW89xK+BF3TZdWL9+PdevX8/I8IQQBiKJghBCCPEK7du3Z/fu3TjlcuP3AxcNHU6GKvBZKSApIVKr1e98Xv123VAoFPw2d25GhSY+Mgql0iAfIm3kOyeEEEL8R2JiIuvXrwdg9o4TKXZJ/pjo9Xr+WjaPdqXyAGCbw+n9zkePXq/n3r17GRGeEMLA3v2xgRBCCPGJMDU1Tf78xaP7uLp7GjCajLPop+Ec2LwaAEtbO2btOPFe5yfIQmbxngyxZkDJxzttMKPJiIIQQgjxHwkJCcmfD25a3YCRZBxtQnxykjBg8nwWHrqavPP0u8rpkR9rO3u879xh2bJlPHr0KCNCFUIYiCQKQgghxH/o9Xpmz55t6DAyTHxsDF+VyZv8dcV6jdPcV++fZ2Nul4Nu3bpRomTJ9AhPCJFFSKIghBBC/Mfu3bsZOHAgAB2/HWPgaNJfx/L/TqX6/dDVD+qreKUa/LBkM8553HFxcf3Q0MRHTqFSGOTjfRw9epTGjRvj6uqKQqFg69atKV7X6/WMGzcOV1dXTE1NqVGjBjdu3EjRJi4ujv79++Pg4IC5uTlNmjTh6dOUu52HhITQsWNHrK2tsba2pmPHjoSGhqZo8/jxYxo3boy5uTkODg4MGDCA+Pj493o/H0ISBSGEEJ+00NBQ9u7dy8qVK2nZsiV58+ZNLov6ecuvaNSpl4EjTF9R4WHJn4+YtxorW7sP7vPk7r/wffyQ1atWfnBfQhhaVFQUxYsX57fffnvl61OmTGHGjBn89ttvnDt3DmdnZ+rUqUNERERym0GDBvHnn3+ybt06jh8/TmRkJI0aNSIxMTG5Tfv27bl8+TK7d+9m9+7dXL58mY4dOya/npiYyBdffEFUVBTHjx9n3bp1bN68mW+//Tbj3vx/yGJmIYQQ2Zavry85cuRApVKl6fwePXqwePHiFMdyuOSkUMlyNOnah9LV66RHmFnKvDGDAdAYm1C8Uvqsv3BxS5rGdO3aNUrK9CPxBklP+DP3ObUC3Xu1b9CgQfLDgv/S6/XMmjWLUaNG0aJFCwBWrFiBk5MTa9asoVevXoSFhbFkyRJWrlxJ7dq1AVi1ahW5c+dm//791KtXj1u3brF7925Onz5N+fLlAVi0aBEVK1bkzp07FCxYkL1793Lz5k2ePHmCq2vSaN306dPp0qULEyZMwMrKKq3fkncmIwpCCCGypdu3b5MrVy4KFSrMpk2b0One/Wbg4cOHKBSK5CSh47djmLvnLOuvPOO33Wf5cfmfH2WSABAa6A9AXq/P0q3Pl1WhDh06xLNnz9KtXyHSU3h4eIqPuDRU7Xrw4AG+vr7UrVs3+ZixsTHVq1fn5MmTAFy4cIGEhIQUbVxdXSlatGhym1OnTmFtbZ2cJABUqFABa2vrFG2KFi2anCQA1KtXj7i4OC5cuPDesaeFJApCCCGyHb1eT+/efUhMTERj40CrVq0oU6YsK1euJCQk5I3njRo1irx5/13IO3XTfhp16oWDc87MCN3gXlY2qtG0Tbr1aePgSJWGzVm+fDk//vhjuvUrRHrKnTt38noAa2trJk2a9N59+Pr6AuDklHLPEScnp+TXfH190Wg02NravrGNo6Njqv4dHR1TtPnvdWxtbdFoNMltMppMPRJCCJHtrF27lsOHD/H9vNWUqFyDmxdOs2n+dDp16oRKpcLe3gFjExO+aNiA3r17o9FoGDFiBMePHycoKAiAn1Zuo8BnpQ38TjJXbHQU3leSnkS6FyqSbv0qFAr6TpyDqYUVixcvJiEhgTx58tC0aVMKFiyIt7c3Z86c4enTp1hYWGBsbEzx4sWpVatWusUgsgdD7qPw5MmTFNN1jI2N09ynQpHyPej1+lTH/uu/bV7VPi1tMpIkCkIIIbKVsLAwBg8ZQvnaDSlRuQYAXqUrMGbxRoL9XnDx2AFCAwOIighl/abNLFiwIPncvIWLUb5Bc9oPHImRJu03CdmVsalZ8udrZk1k5II16dp/x6FjsLSxZd3KhcTGRDN+/PgUr9vYOxATFUVcbAyQVF2mYsWKqNVyOyIynpWV1QfP63d2dgaSnva7uLgkH/f3909++u/s7Ex8fDwhISEpRhX8/f2pVKlSchs/P79U/QcEBKTo58yZMyleDwkJISEhIdVIQ0aRn0whhBDZyujRowmPiKDzsNRTXOycXKj9ZYfkr78aNIorJ49w4cg+6rTqRN7CRTMz1CxHoVDw08pt/NCxCfdvXE73/o00xrTqM5SW3wwhUZvAjbMn8H3yEDMLK5xzu1OgRBniY2PoWfMz4mJiqFatGvnyeTJ79iy8vb1Zv34DMbGxTJs6hTp1Ps41Ip86hUKBQpm5IwoKXfpdL2/evDg7O7Nv377khfvx8fEcOXKEyZMnA1C6dGmMjIzYt28frVu3BuDFixdcv36dKVOmAFCxYkXCwsI4e/Ys5cqVA+DMmTOEhYUlJxMVK1ZkwoQJvHjxIjkp2bt3L8bGxpQunTmjoQq9Xq/PlCsJIYQQH+jSpUuUKVOG9oNG0bjzN4YOJ1vS6XS0K5kbM0srlhy7aZAYYqOjiIoIw/vyeX4d3ifFa0qVCk9PT8aOGUOzZs0wMzN7TS8iOwkPD8fa2ppdZcphnskjSFFaLQ3OnyUsLOydRhQiIyO5d+8eACVLlmTGjBnUrFkTOzs78uTJw+TJk5k0aRLLli0jf/78TJw4kcOHD3Pnzh0sLS0B6N27Nzt27GD58uXY2dkxdOhQgoKCuHDhQnKVtgYNGvD8+XN+//13AHr27Imbmxvbt28HksqjlihRAicnJ6ZOnUpwcDBdunShWbNmzJkzJyO+VanIiILIsuLj47lz5w4uLi44ODgYOhwhhIHpdDr69OlLLo/8NGjf3dDhZDv+z55gYmbOrtVJlZ4M+ZzQxMwcEzNzKtZrgm0OJyJCg3HM5Ybv4wcEvnjGqunj+eqrr2jTpg1//PEHGo3GYLGK9KVUKVFmcnlUpf79rnf+/Hlq1qyZ/PWQIUMA6Ny5M8uXL2fYsGHExMTQp08fQkJCKF++PHv37k1OEgBmzpyJWq2mdevWxMTE8Pnnn7N8+fIUpZxXr17NgAEDkqsjNWnSJMXeDSqVip07d9KnTx8qV66Mqakp7du3Z9q0aWn6PqSFjCgIg4mJieG7777j5q3bXL16BRcXF5o0boyFhQWLFi8hMCCAiIhwIGnI7uW8QCHEp2n58uV07dqVsUs24VWmoqHDyVYW/zyCfRtTboY2evEGipSpZKCIXk+v1/P0/h02LZjB2f1/U6tWLfbu3ZvmvTJE1vByRGFP+QoGGVGod+b0O48oiH9JeVRhEDExMXTo2JG5c+dyNygaq3zFCYhT8Msvkxk5ciQPfO7jVqURuUtVBWD27NkGjlgIYUihoaF8N2wYlRs0kyThPcXFxCQnCc558mJpY0fjLr2zZJIASXPYc3sWov+k33Ar6MXBgwdp165dih1thRCZQ6YeCYPYvHkzWzZvplz7gRRv2iX5uF6n48XNC9i55cfYwpqTy5IWBh09etRAkQohsoLRo0cTFR1NhyE/GDqUbGfRz8MBaNNvOM2+7m/gaN6d2kjD2KVbWDR+GBs3buTZs+csXryIhIQEnjx5Qu3atYmPj08x3UNkfUk7M2fyYmZ95l7vYyKJgjCIggULAhDu/zTFcYVSiWvRsv+89oybe9YDZOp8PCFE1hIcHMzcuXNp1KkXdo4yBfF9BPo+49iOzQDZKkl4ydTcggGT51G9aWv+mDwGLy+vlK+bmnLr1i3c3NwMFKEQHzdJFIRB5MqVC4Do4IDXtrF0cMHMNgfRIQFUqlSJtu3asfD33+XpkRCfmL179wJQoHgZA0eS/ayZnbTzbP9f5hk4kg9TvFINxq/czsEta8jpkZ+w4EAWjhuKs7MLNjY2hg5PvAcZUcheZI2CyHRxcXGMGjUKgIK1mr22nUKppP283dT/PqkCwLq1a1m9enVmhCiEyCIuXrxIt27dqFSvCeU+b2DocLKdh7euYW5lTaX6TQwdygczt7KmcZfelKpWG3vHpJry8+bNxdra2sCRCfHxkkRBZJrExERCQkJo3bo1y5Yto0Tzr3ErU+ON5yiUSmxz5Uv+OiEhgbi4uAyOVAiRFfj7+9O0aTNcPQrQe/wMFAp5Kvg2Op2Oi0f34//0MX+vXsyzB/fImTe/ocNKdzfOnwTg4MGDr9zdVgiRPqQ8qsgQV69e5fTp04SGhqLX6/Hz82P27NnodDoASn3Zi9Kt3m2zJL1Ox8VNv3P7wGaiQ4NQqVT07duXwYMH4+7unoHvQghhSLNnz2bIkCHM3X0WOycXQ4eT5T2+e5tlk0Zx88JpAHK45iLg+VMWHb6GhY2tgaNLX/FxsQxr+Tl+Tx+Rw9ERP19fgyaSERER3Lx5E0dHRxwcHDh9+jR79uwhMjKSIUOGUKBAAYPFllW8LI96oEYVg5RH/fzwcSmPmgayRkGkuzlz5jBgwAAATMwtUShV6BK1OBYojkuRMhSs0QxLR9d37k+hVFK6dW9KftmTR+ePcOfAFn799Vd+/fVXOnfpwvJlyzLqrQghDMjS0hKdToeVnWy4+DYnd//Fb6MGovy/vQZU/9yMmVl9fFNzNMYmDJ6xiInftCc0JITg4GDs7e0z7fpPnz5lx44dLFq0mMTERLzvehMTHQ2AmZkZ0f98DpAnTx5GjhyZabEJkZ5kREGkq5CQEOzs7Mj5WQXqfjcTtcYkQ67z7NoZ/v45aUQiPDxcFjgL8ZHR6/UMGzaMadOm8fuBS9g4OBo6pCxJp9Px19LfWDdnMo458zBh7S4GNChPTFRkcpuvf5jM5y2/MmCUGefqySNM6vMVQ4YMYfr06Rl6rYCAACZNmsT+/Qe4du0qAB6Fi5G7gBf2Ts4Ur1yLS0f3s3XJHADsHRzo1LEjP/zwA7a2H9eITlokjyh8XhWLTB5RiNRq+fzAMRlRSAMZURDpRq/XU7tO0jbk5b8alGFJAkDOYuWp3udHjswbS/0GDZg1cyZeXl6Ym5tn2DWFEJlj69at9OrVC39/f9wLFsHEzMLQIWVJiVot04d8zYUj+yhQvAxjl21BqVSy4MBlOlfwTG6nMc6438WGpEtMZO+GFajVatq2bZtu/er1es6dO0d4eDjFixcnIiKC6dOns3LlSiIiIqhUvyk9W3Tks0rVsXN0STHlafOCGbi6urJ48WLq168v62pEtieJgkg3K1as4OKF85Rp0xd794IZfr38VRsR8uQ+Fw//Rbly5VAqlcyfP5+ePXsSFhbGkSNHMDMzw87OjlKlSmV4PEKI9NG7Tx/8/f0ZtWAtxSpUTZebrfjYGPyfPcHMwpKrp49ibmlNsQrVUBupURtp0iHqzBUfG8OsYX24cHQ/zXsMpHXf75Jf05iYoFAq0f+zJqxqo5aGCjNDndq7nQuH9zJq1CjKli2bLn0mJibStVs3Vv7xR6rXCpYsR6PO31CmRt1Xnvvw9g28r5zjm549adBAKnS9jlKhQKnM3ARKKQlbmkmiINJNVFQUAHlKVc2U6ymUSsp3GEypL3vx8OxBvA9vo1evXoz+4QfCQkOJj49Pbnvq1CkqVKiQKXEJIT5MmdKluf3wKZ9VrJYu/d04d5Jpg7oRHRmR6jW1kREehYvRZcTP5CtSPF2ul5Gunj7K/o2ruHLyMHEx0bToOYhWvb9N1W7A5Hks/+UHRi1cb4AoM4eH12eo1GrWrV9P06ZN05ws6PV6Vq5cSVhYGL9MnszzZ89oP2gUJat+jveV81jb50ClVlOsfNXkdR+vsnzyaCzNzRk3blwa35EQWY8kCiLdFC1aFICE2JhMva6RiRn5qzXCo2JdLm5eSGSgL/YaY/JVqo9el8juX/pTsWJFZs+ezY0bNwgJCWHQoEFUqlQpU+MUQrybGjVqsGvECC4c3kvp/3t6e/fqRc4c+Juc7p6YWVpy4+xJHt+9SVxMNDly5qHqFy0pWbVWihGC84f3Mvu7b6hcpTKDBw0iODg4eRffgwcPEhoayt59+5g+uDsT1+7Cxj5Hpr/fdxUa6M/UAV1BoSBfkeK07DWYIuUqv7JthTqNqFCnUSZHmLlc3Dz4edVOFv74LdWqVePatWt4enq+/cT/ePToEZ07dwbAObc7P63chmexpFHoXPnevVpRvqIluXPpHPPmzeP7779/7ziEyIpkMbNIN1FRUVhYWFC9z3gKVG9s6HCSBT++y4GZ3xH6/FHysa5du7J06VIDRiWEeJ3w8HA6duzEtm1/0aRLH1r3HcqSiaM49Oda7B0cCA4KQq/Xk9cjH5UqVsDKyopTp05z+fIlXHK703/yPPIVKc6hretZNP47mjZtxpo1qzE2Nn7l9Z4/f06p0qWxcnTl69G/4FbAK0vNLdfpdFw4so9NC2bw5N4dpv95CKfc7oYOK8uIjYlmWIua1K1Vg1WrVuHn50dUVFTSGhd3d5ydnVOdExERwdatWylcuDDr169n2rRpfP3DZCrVb4qpedrWxMTFxDC0RU30CbGcPHFCSqL+x8vFzIfr1cDCKJMXMydoqbHnsCxmTgNJFES6yuHkRJ5qzd95j4TMEhsRSuizh6g1xlzdtoz7p/aR1yMfNapXY+HChagzuQKDEOLN9Ho906dPZ8SIEShVanSJWhYsWEC3bt0IDw8nLCwseWTgpcuXL/P11z24fOUyVb9oyeG/1lO9Rg3279v31p/xkydP0rhJE4KDgnB186B83UY06dIHMwvDVVTT6XQc2LSKHSsX4vv4AabmlnQcOoaazdsZLKasavaw3vhcPkOZsmXZ9fffvLy1USgUFCxUCFcXV9zc8nDo8GHMzMzwuX+f2NjY5PPL1KjHt7OWfHAcT+97813LWrRp04Z169Z9cH8fE0kUsidJFES6qlixEoFGdtTs97OhQ3ktbXwc947t5Ozq2cRFhfPrr7/Sv39/Q4clhHiFEydOMG7cj/Tt24dmzZq9tX18fDxjxoxhypQpNG/ego0bN6BUKt/pWvHx8Rw4cIANGzawfPly2vQbRoseAz/wHbw/vV7PpWMH2LZ8PrcunMbOyYWWvQZTq0X7TI8lu3hy7w6bF8wgJiqS4pVq4OLugam5BXNH9ken02Fjn4Ow4EB0Oh0lqtTCytaeklU/JyoiHN/HD6hQ54t02dRvwZghHNm2gX379lG7du10eGcfj5eJwtGGNQ2SKFT7+5AkCmkgiYJIVy1atODEVW+aTFiZpYbuXyUhNoa1ferh4piDmzeuY2pqauiQhBDpxMfHh9y5c2NkZJSm8ytUqMiZM6f5vOVXtOgxEAeXnOkc4ettWjCTjfOnYWJmTtNu/Wj2tTzISCtdYiJ6vf6Ni5A/+Bo6HTGREexcuZA/F82mX79+zJkzJ8Oul11JopA9vdtjFiHeUcOGDfG/f4P7J3cbOpS3MjIxpWz7gTx84MPq1asNHY4QIh15eHikOUkA+OOPFbRt25YDm1fTt345tAkJ6Rjd6wX7veDv1YvJ6VGAZSfvSJLwgZQqVYYmCQ9uX6d/vbJ8Xa0Ify6aTaNGjZk9e3aGXU+IzCaJgkhXrVu3BiDoobeBI3k3bmWqA7By5UoDRyKEyEoKFCjA8uXLMTMzA+CrMu488r6Z4dedNrg7UeGhdB3xU4ZfS3wY38cP+LFLc8xNjVm7di0HDhxg27a/3nmq26dKoVIY5EOkjfxv/sTcv3+fKlWrksfNndGjR7Nnzx7Sc/bZ06dPATCxsE63PjOSmY0Drl6luXb9evI+EEIIAWBsbExUVBQDBgwAYFirOlw/czxDr+lVNqls8+1LZzP0OuLDXTt9jLjYGGZMn07btm2pVatWlp9yK8T7kkThE7N//35OHD9OeAJMmDCB+vXrkzNXLnbu3JnmPgMDA/H19eXSpUvUqFkLW1d3CtRoko5RZ6yy7QcSEhxM4yZN0zVpEkJ8HGbPns2BAweoWrUaUwd1JeD50wy7VvuBI8mTvzCb5k+nXYlc7N+4KsOuJT5M3sLFANi9O+tPtc1KFCqlQT5E2shi5k9ITEwM5cqXJyBGR5MJq4gOCeDWvk1c27kyeZO0mrVqUfyzz6hevTpqtZpTp06hVqtRKpXY2tpy+vRp6tSpQ8eOHVGpVMycOZPvvvsOnU6XfJ3Ws7Zi7eL2ujCypNsHtnBs4U80a9aMyZMnS/1rIUQqERERWFlZ0bDD13T+7scMu050ZAT9v6hIZGgIAI06f8NXg0cDsHvNUo5s28DYZVvQGJvINBcD0SUm8n3b+jzz8WbevHn07NnT0CFleS8XMx9vVtsgi5mrbN0vi5nTQBKFT8jJkyepXLky9Ub8Sp6SVZOP63SJ3DnwJ48vHiX4kTeRQX6pzlUoFKBQoFIboY2PI2euXNjZ2XHt6lVyFa9IruKVsXLKicbMEhev0pn5ttLNxU2/c2HjAkxMTTl75gzFihUzdEhCiCzG09OT+/fvM2z2shS7RmeESX07cvn4QdQaDSvP+gDQrkSu5NeNTUxZcPASJmZp2yBMpN3ti2f4sVtLZs+enTw1TbyZJArZk+wy9QlZsmQJ5rYO5CpWIcVxpVJF4TpfUrjOl+j1eoIf3wW9Hp1Wi0O+pB1K4yLDUSgVaMws8Tm1jwOzhuEfEES1b8ZSsGYzw7yhdFbqy14UrtOKVT0/Z8SIER80HUsIkf3o9XoCAgJwdHR8bZutW7dSrFgxti2fn+GJQtt+w7l8/CDa+HjiY2OZ8W2PFK/HxcYwvFVdZu88maFxiNSunjoKQKdOnQwcSfajVIEykxcXK3VvbyNeTRKFT8StW7dYunQphT5viVL9+pKBCoUCe7fU026MLf7NwD0q1sHKaQ1GpmbZborR25ha2+FZuT5///03p06domLFioYOSQiRzp49e8a6devYf+AApUuVwt3dnYcPH/Lnn39y82ZSZaPTp09Tvnz5VOd6eHgAcO/65QyPM2/hogyauoBZ331D5wqeQNIoQpGylbhx/hRxMdGEBQVkeBwitaf372Bubo6NjY2hQxEiQ0mi8IlYu3YtppbWVOwyNF36c/AonC79ZEUVuwzj3ondXLhwQRIFIbIprVbL6dOnef78OTVq1MDBwYH58+ezf/9+tm7dikKhwLNYSQ7sn0pCQnyq8ytUqICTszNly5QhODiY3LlzExcXl7xwtUyNOpnyPirWbUx4SBBLJ44CkkYRLh47kPx65YbNMyUO8a/w4CACfZ9TpmxZQ4eSLSmUChTKzB1RyOzrfUwkUfhE7D9wAMcCxVFrTAwdSpYXFx0BwJYtW+jXr5+BoxFCpMWIESOYPn06AB758mGkVnPnzh2s7expN+B7KjdoRg7XXOgSE4mOikClUhMWHIhzbnciw0JYN2cyT+57s2PHDpxy5cEvLIr7N64AULNZW1r3/S7T3ktYYMpRgyLlq1ClQXNsHZ0pXql6psUhkgxrVZuIkCAmj1tn6FCEyHCSKHwCLly4wKmTJ6k18BdDh5ItKBRJVUSKFCli4EiEEO8iISEBtVqdoob9rt27MTW3oP/EOSz7ZTTahARa9BhIm37DUpyrVKmwsLIBwNQ8aVGwhbUtX49O+n2p0+mSKwsFvniGhbUtJv9swpZZXu4s/OOKvwAoUDx7Foz4GHhfPk9YUADlypWjVatWhg4nW1IqlSgzuVypMlGqg6WVJAqfgJGjRmGX05285T83dChZni5Ry5mVMzAxNWXs2LGGDkcI8QZnzpzh77//Zvr06SiUSpycnDE3Nyc0NJTHjx7SbeQESteo+0GLjv+//KiDS870CPu9vazXv3Pl7wyetjDV62HBgTy9d4fCZSpKudQM9vL/wNmzZ9Hr9bLBmvjoSaLwkTty5Ah79+yh1qDJKFXyz/02QQ/v8PDcIbp06YKDg4OhwxFCvMb69etp27YtxiameBYrSbEKVYkIDSE+Noa8FlYU1+uoWKexocNMF6Wq1aZWi/Yc3LKGjmXzAXpUajUJcbGYWVoTGZa030KufAWZuvnAmzsTH+TF46QytUZGRpIkiE+C3Dl+xPR6PYMGD8Ypf1E8ytc2dDjZgoOHFzaubty4cYPExERUKpWhQxJC/MeWLVvo1KkTZWvWY8j0RSg/8p9TnU7Hyd1J044URkYkREeiTYhHpTHGJm8RHM2t8DnxN0/v3zFwpB+3veuWs+yXpI3vevfubeBosi+FSoEik8ujZvb1PiaSKHzEbt68yeVLl6g3/FcUMhz9ThQKBaXb9OXAzGEcO3aMGjVqGDokIcT/OXXqFG3atKFIucoMnDI/2yYJQX4v2L9pFdWbtMI5t/sr2+zbuJIVU8eREB8Hej2mNg60nrvnlW0j/J8ScPcq104fo1iFqq9sIz7M1dNJeyds27aNxo0/jtEqId5GEoWP1LVr12jWvDlGxia4Fi1n6HCyDZ02gUfnjwBglskLFoUQbxYZGUmbNm3JV7QEw35dgdro9XvCZHWDm1QjLjaaLQtnYWljx687T6DRmHBq73aKlKuMnaMzT+97kxAXS4kvv8E+b2FcP3t9ueaoID8ALh8/KIlCBggJ8OPW+VPkcXOjdm0Zof8QCpUSRSYvZs7s631MJFH4SM2cOZNHjx5Trc+PqDXGhg4nW9DrdBycNZwnl4+zbNkyypWTBEuIrGTPnj08efKYiZMXZOskASA+LgYAjbkVEaHB9KlbDqVSQVREOGqNMfN2n8XFPWlzN9diFcjhWeyN/bkUKcv9YzswNpUHHOnt6PZNzP9hEACTFy3E1NTUsAEJkYkkxfpIxcbGYpvTHc/KDQwdSrZxdcdKHpw7xJ9bttClSxdDhyOE+I+XN2gPbl0zcCQfZsI37dHr9dh7ePHlr39TodtI4hPiiYoIx9QmB9r4eH7o3IQTf28FwMzO6a19FqrTGoC/lszJyNA/SS+ThLlz59K2bVvDBiNEJpNE4SMUGhrKhg0byFmisqFDyTYStQlc2vw7ffv2pVGjRoYORwjxD71ez9atW6lUqTJt2rTB2s4+20+tuXHuBABBPjdZ27MGVs65aTHjLyp2H0WLmVsp23EIgX5+eF85j1OhkpjZvL0Cm8o4aTNNnU7H3vUrMjT+T0lsTHTy53369DFgJB8PhVJpkA+RNvKd+wi9rNiT8w3zWUVKAfeukxAbQ9euXQ0dihDiHwkJCQwYMIDmzZtz79Fjytdryo/Lt+KUy83QoX2Qn//YhleZihQsURZ9opabu9diZpuDArVaoNaY4FW/PU1+WUf5riOo9e2sd7rJsc2VjzrfzwNg2aRRjP+6FTqdLkWbe9cupbjxFW8WEuDHdy1qAmBvb2/gaIQwDFmj8BFyd3dHbWTEsfljKfvVIPKWq4VSnb3n82a0cN8nAKn+sAoh0p9Op+Pu3bvkzZsXjUaT6vWgoCBOnz7NpEm/cPrMabqNnEDd1p0/mrr1HkWKM3bJJnQ6HV+VycvTi0d5ceMcLkXKJrexcsqNlVPu9+rXtWh5ag+fw/7J/bl1/hT9G1SgfruuREWGc/HwXp7cu4NSpaLvhDlUqt+EG+dPMm/kAKIjI4iNjsIplxuzdpxI77ebLR3csoZF4//dxfvGjRsGjObjolQZYGdmWcycZgq9Xq83dBAibeLj41m/fj2HDh2iZs2adOzYMfm18+fPM3zECA4eOIB7mRrUHjrjo/kjmxHCXjxi35SBEBtJgL+f7J8gRAaZOHEi06ZPJyQ4GEsrKxrUr09sXByBgUEYqdUEBwdz7dpVABycXek/aS6FSn28hQX+Xr2YFVPGAXoaT1iDnXvBD+4zLiqczYMakxAd+fpGCgX88+ffqXBp/G5dAGDt5acffP2sSK/X8+TebXJ7Fnqnv4XtSuQC4MSJE1SqVCmjw/skhIeHY21tzcVuTbDQZO7Dy8j4BEot3UZYWBhWVlaZeu3sTkYUsplHjx5x4cIF9u/fz/z585OPL1u2jOjoaHLlykWpUqUwMjJi965dzJ8/n4EDBxIdEoC5naMBI8/arF3cqNJrHNvHdqVp06bs2LHD0CEJ8dEJCwvjp59/xqNICXqM7cn9G1e4cuYYphZWWNo6oU1MxMHOhT6tu1KoVHkcc+b56B9wNPzqax7eus6R7RsJ9LmRLomCsbkVrX79m5Cn9wnwvoJLsQokREdi6ZSLoAe3uH9sJ5ZOOYkK8qNAzeY4e5VhxVelATjy1waqN22doj+tVsvJXVspWr4Kdo7OHxxfZtPr9cwd2Z8Tu7ZSvk4juo+ahKWN7WvbB754lvy5JAkZwADlUZERhTSTEYUs7NGjR+zcuZPKlStz5coVfpk8hVs3/x3+tHHOTclWvclboTb7p3/L44vHUpyf18ODHA4OnD17li4rTmBkImXz3kQbH8e6fl8QExaEQw5HzMzN+H74cDp06MCtW7coU6bMR3/TIkRGiY6OpmXLLzl67BhTtxzEwTmnoUPKMnatWcLyyWOwdMxJi5nbDBLDk4tHOTh9cPLXn7fqgN/jR1w/8+/fFYVCSY6cuWn+9QDK1KqPhZW1IUJ9L6FBAfSvVw6tNgEAtVpNYmIiHl6fUbhMRYqUq0yhkuUwMTNPPufUnm38OrwPFStW5OTJk4YK/aOTPKLQoxmWmTyiEBGfQKlFW2VEIQ0kUcgiTp8+jampKdevX2fjpk2cPn0aP1/fFG0s7Bxxr1gHjwp1ccxfLMVNqy5Ri9+dK4Q8vQ+AqY0Djy8eJfTJPTyrNaZIvTaZ+n6yq2fXznBh4wIcPArz9PIJwl48Tn6tUaNGNGvWDF9fX3Q6HfHx8VhZWfH1119ja/vv0ym9Xs+zZ89QKBS4urpKciGylRMnTrB27VpsbGzw9PTE2NgYnU5H69atMUrj3gVXrlyhc5cu3PH2ZvC0hZSoXDOdo87e+jesiP+zx5TvMjy5zKkhBD/yZvvIdqmO5yxeGWtXd0Kf3uf5tdPJx6dvPYKre77MDPG9rf9tClsX/0qZMmU4cuQIgYGB7Nu3j4MHD3Lw4CF8fV+gUqvJV6Q4XmUqUbhMRSLDQpgzoi/Nmzdny5Ythn4LHw1JFLInSRQMLCEhge+//57p06enOO7iVYYc+YqQq3hFokMDsXcriG0uDynxlYn0ej3Pr5/F9/ZlQp7ex//OJaJCAjG1tEGlVqNUGxEdFoQ2Pp4cjo645syJVqvl+bNnhAQHJ/dTqnQZzM3NsLayol69euTNm5eGDRtKAiGylKioKPr07csfK1aQwyUn4SHBxMXGJL9epUpVdu/ehbm5+Rt6Se3vv/+mefPmOOfJS48xUyhQvEx6h56t6XQ6Opb3xKlwGWoP+9XQ4QCQEBOF351LxEeF41qsIiZW/z4I8b11kUsbfsPf+woKhYI1l54YMNLUtAkJaBPi2bZsHvdvXObGmeN45MvH6VOnsLOzS9FWr9fj7e2dlDQcOsThQ4cJDAxIfv3evXvky5e1E6Hs5GWicKlXCyyNMzlRiEug5O9bJFFIA0kUDCgxMZHChb24e9ebAjWaYuWcG7vc+bBwcME+HeapivSl1+tJTIhDZWScfJMfHRLAjT3rSYiJIiEmGr1eh5Vzbqxd3IgK8iPwwS0AYkICeH7zQnJfHp6ejBg2jB49ehjkvQjxXz179uSPlavoOuJnqjdtjVKpRJuQQHxcLA9vX2dyv06ULl2KH8eNo1q1am8cXYiNjWXgwIEcOnyEu9538CxagnHLtmAku8Sn4nPjCt+3b0jh+u0o13GoocN5Zy/XNLTq8x0teg40cDQQGx3FmlkT2bch5R4SXl5enD9//p12U9bpdOzYsYMdO3Ywc+bM906KxZtJopA9SaJgQFqt9t8/tgoFZtZ2tJu7S0qZfsTCfZ/gf/86FzctJOz5Q8pXqMDQb7+lefPmUmlJGMzx48epXr06Hb4dwxcdXp28XjtzjBWTx/DkvjcATk7OVKtejSJeXlhbW2NjY4Obmxvm5ua0at2ax48eUaZGXUpW/ZxqjVqiMXn7jdqn6PzhvUwd2BU794I0+mlVthg11sbFsLp7VYw0GqZuOoBTbndDh8TcUQM4vjNpmlDPnj1p3bo1Xl5eODo6yu/WLOJlonC595cGSRRKzN8kiUIaSKJgYOHh4TRq3JhjR48C4F6uFhU7D8XCwcXAkYmMpNfpeHThCDd2reH5jfO4583LpIkTadmyZZrngQuRFnq9Hs/8+dFY2TF2yWaUb7ip0uv13L9xhYe3rxP44inXTh8j2P8F0RERKTby8ihclF4/zsC9YJHMeAvZXpviSQu7m8/Y+t57JxiCNi6GtT1q4F6oCBNW7zR0OACc+PtPfhvZHzc3Nx48eCBTO7MgSRSyJ0kUsgi9Xs/06dOZMnUaejNrmk1amy2eLIkPF+Bzkwvr5/Lk8km+/LIVCxf+nmJxtBAZ6cqVK5QoUYKR89dQvFL1NPejTUjA59ZVQvz9KF29Nmqj1BupiVdrX9qNRK2WzqsvvL1xFrFzTCcC7/9bhc/Y1IyyNevRa/xM1GrDVF7fu34FyyaN4vbt2xQsKNN3sxpJFLInuRPNIhQKBUOHDqXDV+2J8H+GTpdo6JBEJsnh4UX97+dSo+9P/PnXVnLnzs3ixYtJTJT/AyLjrVmzBoAcrh9WrlRtZESBz0pTvnZDSRLeU97CnwFwduU0XvXsLiEmilu71+J96M/MDu21rF3d//08pwdxMdEc//tPpg/q9sr2G+dPZ8aQr9HGx2dYTNWatEJtZMS+ffsy7BriwylUShQqVSZ/yO1uWsl3Los5dvw4dm4FUck6hU9O/mqNqNprLBaueenRowee+fNz/fp1Q4clPmK3b99m6tSp1GzeDld3T0OH88kau3gjphaW3Nq9Ft+b55KPJybE43NyN38ObcHZldM4tfhnEv6vEpUhle8yghYzt9F59QWaTdmIc+Gkalb12nVN0S48JJhBTaqy5feZnDu4m7FdW2RYTCamZuT/rDT79u/PsGsI8amRRCGLqV6tGtFBLwwdhjCQ/FW/oMnPK2k2cRWROjUVK1WiZ8+e7NixA51OR1BQEJcvX2bnzp1EREQYOlyRza1Zswa9Xk+z7v0MHconTWNiwi/rdgMQHZxUnjPsxSO2fd+WY3NHYaSE3J6FALi1e43B4vx/RiZmWDr+OwrlVDipCtLZ/X8nHzu6fRO9an6G3+MHFGvaHdvcnjy4eTVD4ypaoSqHDh1Cq9Vm6HVE2in+2Zk5sz9E2sh3LospVqwYYf4viA4NNHQowoBy5CtCneFzyFWuLhv+2knjxo1RqVQ4ODhQsmRJGjVqxLx58wwdpsjmPv/8cywsLfl1WG9Dh/LJu3vtIgDnVk1n24g2bB3agkj/p/SbNIdFh64wcv5qXNw8uLRxHr63st5ahuItemCT04NDf65l/8ZVAAT5Pf/39eZfY2xpg16vy9A4ipWvQkR4OOfOnXt7YyHEW0mikMW8rNSg1pgYOBJhaBb2TlTtOZrWc3ZSZ+gMKnQaSu0hU8lboQ4ALVu2NHCEIrurXr06c379lQe3r3PnstxYGVLJSjVxzeuJRq1CpY2lfrtuTNmwj6oNk6bq2Dk6M23LIZQqNXt+7om/9xUDR5ySQqHAq+FXACyZMIIn973xf5q0s717+TqojDRozCxRKBQEvniWYXF4eBXHzMKSAwcOZNg1xIdRKpUG+RBpI9+5LMbBwQEA7yPbDRyJyErcy9ak2Bdfkbd8bYwtkio23LhxQ6YfiQ/Wrl07ypQpy8xve3Bq7/ZXLqYVGc/CxpaZW4+w+Mg15u4+Q9cRP5HbM2XlHrVajVv+pClIJtZ2r+rGoDyrN6V486R9OIa1rMXhresAKPB5UrJTqk1fVBoTJvX+6p37PLl7Gz/3asulY0k3/psWzKBv3bKEhwS/sr1KrSY6MoKDBw99yFsRQvxDEoUspmHDhjg5OfPgjFRtEK9mbucIQLNmzRg8eLCBoxHZnbGxMdu3b6NEsWLM+u4b5o8ZLMlCFuaUJy8AN3dljbUK/0+hUFC8ZS/qfj+fkq36UHPwNDosO4lLkXIAWLvmpcgXHXn+8B5bFs5+a38Lf/yOOSP6cOPMcab078ycEf3YvGAGwf4vuHjk1X8jQwL8ADA2lspbQqQHSRSyoKZNm+B76xLRoUGGDkVkQaVa9qT76rOUaduPpUuXcu3aNUOHJLI5R0dHDh48wMqVKzmybSMHNq82dEjiNfpP+g2lSs2dfRuIiwwzdDipKBQKXIqW47Nm3clTpiYqjXGK1wvWbgXAxnlT2Th/evLx8JBgNv8+k1HtG9KhbF7al3Lj0J9rASjVui8AJ3dvTW5/9+qr12kEPH8KQPv27dPtPYn0JYuZsxf5zmVB7dq1A+DJpWMGjkRkVUq1EcUbd8LM2o7ly5cbOhzxkejQoQOt27Rh9+rFhg5FvIZarabD4NEAPD5/2LDBpIGptR1VvvkRgENb/h0VGdelGZvmT8fn5lVs8xSgeIselGk/iKaTN5K3Un3s3QvhXrEeLWftAODWhTOv7P/ApqSF1LVr187gdyLEp0EShSyoRo0atGjRggvrfiM2Cz4xElmDUm2ER5WGLF6ylKioKEOHI7K5u3fvUqNGTTasX4+JuYWhwxFvUKJKLQAencueC3ZzlqgCQGigPzpdUhWkIN+kCkmNJ6zhi/F/ULx5D4p80RGbXB5Y5HCl0YTVVO83EYscLiiUSl48us/MoT155nM3Rd8vC4IEBcmIfFYlIwrZi3znsqjffvsNpU7L2VWzDB2KyMK86rUhPCyU1atlqohIu5s3b1K1WjVu3btP86/7M3DKAkOHJF5jzeyJDGlWDYC4iOz5IMnE0oYag6ai1+sZ3KQqo9o3JD4uFju3gti5F3zr+SVa9sLU2p6z+/9maIuaLJnwPQB+zx5x/vAeAB4/fpyh70GIT4VCL6vWsqx58+bRt29fWk7dgF2e/IYOR2RRh+aMJOT2ee5638HW1tbQ4Yhs4tGjR8yZM4dly5cTGhJCHs9CjFywFmt7B0OHJt6gQ7l8JMTFAtBs2hbOLJ+M760LFKrTinIdhxo4uneXmBDPvl/64nf7IiojDY4FSlCsSVdcipZ7p/P1ej3Xd6zg7sE/ifB/ikKhSLUIX25vspbw8HCsra25ObwTlpm82DwiLh6vyX8QFhaGlZVVpl47u1MbOgDxehYWScP/2n/+KAjxKuW+GsT6AY1YuHAhw4cPN3Q4IgvT6/WsW7eO2bN/5cyZ06jUauq26YxTLneqN2mFmYWloUMUb/H1qEnMH5NU7Wzr0BbJx2/tXkue0jVw9ipjqNDei8pIQ/0fFqHTJaJAgeI969wrFAqKNe5C0UadOTB1AL63LuBWthY+J3ZlUMQivSgUyvf+906Pa4q0kUQhC/vzz60AhDz1wTF/McMGI7IscztHHD28uHjxoqFDEVmYXq9n+PDhTJ06lSJlKzFw8jw+q1gNC2sZhcpOajRtTR7Pguz4YyEn/qkCpNSYok/UsmdCLwDcytehUveRaMyz/pNTpVL1QecrFAo+/+5XdAnxqDTGlO3wLduGtaRLB6l6JER6kBQri4qIiKB27c8B8L8n5S/Fm+UpW4sNGzYwadIkGW4XrzR+/HimTp1Kp+/GMWbxRirVbypJQjblUaQ4AybPRWNsAkDedtPJ//US1GZJ/56Pzuxj08DGKc6JDg3Mcrs5pxeFQpFchvXese3ERIRJedQsTBYzZy8yopDFREVFMXfuXH6bO5cn/yzGUqrkn0m8WbFGHUmIiWLkyJHcunWbRYsWYmxs/PYTxUdPr9czZMgQZs2aRdv+w/miQw9DhyQ+UMCzJ1w6cYj4uFhUplYY2+UCoECvFeh1idya3ZyEmEj2TenP50Nns++XvvjeOAtAs2mbsXZxN2D0GSchJoorm3/HyMiIsmXLGjocIT4KcgeaxcycOZMffvgBU2s7Wkxeh0pjjKVjTkOHJbI4hUJB6da9sXZxY83vP+LzwIe/tm7F3t7e0KEJA9uzZw+zZs2iZc9BNOve39DhiA9w+fhBZn/fn+iIMPhn5NC6cM0UbRRKFR4df8Vn5QCeXznJycU/JScJAKbW2Wux+pU/F3N503zsPbywdy9E+c7DUKqNXtn23pFtaONi6d+/vzwoycIM8YRfRhTSThKFLESv11OmTNJCtJiwYC5uWkCdoTMNHJXITjyrNsTS0ZX904dQvkIFrl65gpmZmaHDEgayZcsWevbqRYHPSvNl72+Ta8yLrEmn03H74lnURkZ4FiuJ8j8LPmcO60NsVASOVbuisXYiLugx9mVapupHqfn3Z/7+kW0AWBWsRvido2jMstceGZc3zQcgyOcmQT43uXdkG82nb8Uihws6bQJ+dy7z/NppokMC8Dm+E4CqVasaMmQhPiqSKGQhP/zwAxMmTEj+2kJGEkQaOBUsQY1+E/l7Qm9Onz6Ni4sLL168IC4uDkiqL75ixQqMjIxo3LgxhQoVonDhwri5uaFWy6+Ej4Fer2fx4sX07NmTMjXr0fOHKaluOkXWM3/MYI5u3wSAQqkkj2chvMpU4PrZk6jVamKjIlCZ2eJQpnnSCfkrvbIfIwt7lMbm6OKiQKHAs8sCAs9uBGBjvwa0+i17VAZ6evkEAFOmTKFVq1aUK1eOgIAAYsODeXByFzd3rSL2P3tJdOnShebNmxsiXCE+SrKPQhYREhKCnZ0dALWHTMW93Ofy9E+kiTY+lgsbFnB1+4pXvq5QqkCvQ6/XY6QxJiE+KYEwNjbGI58nRbwK4+XlRZEiRXB3d8fa2hpLS0usra0xMzN75/+XcXFxPHz4EI1Gg4mJCQsXLiQoKAgTExPMzMwwMzPD0tKSkiVLUqpUKTSazK2r/bGJiYnB39+fJUuWsGrVah488KH85w0ZPH2h/C7Jop7cu8MPnZsSFxODLlGb4jWNtRPxYX4pjpm7lSRH+TaY5fR6a986bQKRPmex8CiHUm1EjK83D9Ym7bPQZv5+TKyy/kL2FV+VBpLq71taWrJs2TK6deuG2tgkuWz4unXrKFiwIIUKFUKhUMiUoyzs5T4Kd3/siaVJJu+jEBtP/rELZR+FNJDHh1nE77//DkCzCavI4VnEwNGI7OrW/s2cWj6FxIQETB3dyFXzK4xtHNFY2KLUJFVIUZtaEnzrFN5rf6Lr9z9TvFJ1nj+4zzOfu7x45MMtH2/2HzpMaFBgqv6NjY1xyOFIjhw5cHZ2IoeDAw4ODjg5OZEjRw4SEhK4ePEiZ86e48b1a2i1KW9+cucrQKI2gdiYaOJjY4mJjiJRq8XExIRSpUpTpUplqlWrRuXKlbGxscmMb1m2FhcXx8qVK1myZCmnT58CQG1kRNUvWtJp9BQKl67wySYJgb7PmDtqIKbmFphbWfPioQ/9Jv6Kc568hg4t2dCWtQBQmVqhVqnRRgajNrcj1xfDMMvphTY6jBcH5mH7WX3M8xR/r1rwSrURVgUqJ39t6lwA+9LNCbrwJ+t716bjynPZZpTp5Z5CLVq0YP78BZw79++ai2vXrtGmTRtDhSbER09GFLKIffv2UbduXbzqtKLy1yMNHY7Ihh5dOMreKQMxsrTDs+VQ7ApVeGP787+0xUyjZM7OkxhpUj+FiwgNJsjvBdGREcRERhATFUlESDBhIYGEBwcRHhxEZFgwESHBhAYFEBkehlKpJLdnQdwLFSNfkeLkylcAnS6RmMhIHFxz4VE45X4g2oQEHt6+zp0r5/G+ch7vS+cIDvBDoVCQx82dPLlzU6VKZX7++edsc1PzJoGBgcTHx+Pq6priuF6v58mTJxgZGeHs7ExCQkKKEZaoqCgePnxIzpw5sba2Jjg4mNOnTzN8+Ahu3rxB8Uo1KF/7C6zs7MntWRCnXG6Z/dYMTq/Xc/3scUIDA0iIi+P3H5OenquMNOgSteh1OtwLFmHcsi2Ymmf+PH3fJw+Z98NgHty8ikKpwilXHh7fvQWA1+BtmRKDXq/j1qxmALT5/SAmFtaZct13pdfpiA4JwMzOkeiQADb1b0D58uU5ffp0cpvIyEgsLf/dGPCzzz7jypWPs+zrx+bliMK9n3phaZK5Iz8RsXF4/vC7jCikgYwoZBHFixcHIDYi1LCBiGzL+3DSzUb+L4djW/DtpQE9mg3i1vKR7N+0mgbtu6V63dLGDksbu3e+vjYhAb1e98qk43VeLtr0LFaSLzr0QK/X4/f0EbcunOGZjzcBL57yyy+/EB8fz7Rp096536zG29ub3r37cPjwIRQKBVWqVkVjpMHS0oKg4GDOnTtHdFQUkFTBSq/X4+qak4KFCpKo1XLhwgWi/vM6QJ78hZi8YS9uBd4+FeVjFh4SzPQh3bl98WyK46aOuak4ajUAz078xd3NsxjXrQU/LvsTEzPzd+7/2uljXD97gjb9hqUpYT1/eC9TB3ZNcexlkuDZdeF795dWCoUSx8od8T+xkvXf1KbzqnOZdu23iY+OZG2P6qmO16yZsqqThYUFERERfPnll+zZs4erV69mVohCfJIkUcgibG1tMTE1JS4y7O2NhXiFnJ+V5+HZAwReO/ROiYJdoQqY2Luy+feZ1GzWFpMPrI6kNnp1ycL3oVAocM7tjnNu9+Rju1YvYfqUMeTPn59evXp98DUyWkBAADt27OD27ds8fvyYq9euc/PGdSytbek+ahLhIUE8vH2dWD34+wYD0KhLH9wKeBEdEZ5UG19txIuH9/F79oiw0ABqtuxA8co1iAoLJTw0GGs7B5xyueNW0OujGGn5UAe2rOb2xbM4l2uAW+2vMDKzJPzxbewKlk5uk7NyUyKf3+fh8a2cOfA31Ru3emu/oYH+zB09iKunjgDgc/MqoxaseaeYfG5c4dHdWxzcshbvK+cByPXFMKwKVCH6xW0SYyKw9Mj8Wv8O5Vrhf2Il6HVc3rKIEi2yxr4aWwY3SXWsX79+DB8+PNVxCwsLtm7dSpUqVfjmm28yIzwhPlmSKGQRjx49IjYmhmfXz769sRCvUKBaI27t3UjAxX3YF6mKXeGKbz3Hs+V33Fg0hD3rltG0W99MiPL9NfiqOy8e+fDNN99w4sQJGjZsSM2aNbGyskKr1WJhYWHwefhxcXH89ddfLF26jH379gLgmDM39k6uuBQqTo223fmsYnVsczgZNM6Pke+ThxzYtAoAl/INMHfKA4BDkdT//6MDnwFw7uCed0oUhrSoRVRYCMb2bsQFPeLqqSN8Xb0YeQsXe2XCoI2PZ/sfv7Nuzi8pjiuNTPHsugC1edICYjOXQu/3JtOZc82e+B5ayJXNC3ArVwvbXPkMGk9ifFzyQzKtVktERATR0dGppuj9PxMTE86fP59ZIYp0JPsoZC+SKGQRLVsm1cIu236AgSMR2ZXa2BQj06TpFLrEhHc6x9qjOCaObvy5eA51WnXEzDJrzt3sPOxHzCwtOXRwNytXrgRApVKRmJiIRqOhffv2DB06lCJFMrcQwIMHD1i4cCFLli4lwN+fgsVL023kRMp/3hArO9ns7r+iwsNYNvkHnt73RqlUERkeysTVO7CwTnsFnhcP7xPw/CkASqM3T3tzLlOPkNvnOHdwF4e2rqdyg6ZojE1e2z4uJhoAl9p9MLJ0IPDsJkKu7uLqqSOEhwRjZfvv1Lz1c6eyZdFs0OtRqDXotfE41+qNysQSqwKV3mshckazK9EI30NJU562DW9NvqqNqPLNjwaJ5fa+DZxZPhmAChUqoFKpsLGxkWIGQmQRspjZgB4/fkz3r7/mzh1vnjx+BECXFScxMjE1cGQiuzr022jun9hFhfF/o1S/W/m5iGfeXJvbhxY9BtK6z9AMjvDDhQT4cfP8KWKiIjE1t0h+ohzk94IGDRrSp09vGjRogEqlypDrh4eHs3XrVlauXMmBAwcws7CkaqMvqdO6E7k88mfINT8Wf0wdx85Vi1CqjAA9ukQtleo3ZeDkee/ch8+ta1w9eYRiFaqSt3Axgv1f0LdeOQBqzT761vNvrpqI77ndALgXLMLkDXtf2/bqqSNM+KY9Fh5lydP0BwDCvU/wdOdkqnzRgv4T5wBw6+IZxnVtAUDOhsOwyl8xqQxxFhYb+AiflUk7dZduN5CijTql+zViwoK4tm05MWFB6LVa1CYmqI3NiA72w+/2RYwtrInwTxrlmT17Nv379zf46KDIOC8XM/tM7muQxcwew+fKYuY0kBEFA9DpdJw+fZqu3brhfecOnzXpgnXRBBw8CkuSINIs6OEd/O5cTroJU777j7ZlzgKYuXiyfcUC6rfrluIpaVZkm8OJyg2apTjWtGsfTuzayu41S2jcuDF58rjRv38/2rZtS86cOd948xETE4NKpXrrPg7Xr1/nl8mT2bRxI3FxcRQuVZ5e46ZTqV4TjE3l5/ZtJvb+iisnDwNQYex6TKwdODiwGid3//XaROHU3u3s37gKhVLBsF+Xc+nYAWYP70OiVsvaXyelaKuxsHmnOPI26Erw7TPER4Tw8M6NN7ZVqZPW3Zjk+LekqlWByhifzsPxnVvoPnICi38eyYldfwJgV7Ix1gWrvFMchhZ88S8gaWPPjEgSAJ5fPcWt3UlTtOrUqUNwiB9XT18hISGBwoW9sLOzhQJ5WbN6NXny5MmQGIQQH0YShUwSFRXF1KlT2bV7N3FxcVy5fBmA6n1+pED11Iu4hPivl4N/L29646MjuHt0J4EPbhPoc5Pgx3dRGRlToMO4917gWqDtKK7M7Ma2ZXPpMOSHdI89o6mNNFRv0prqTVpz/8YVdq9dyvcjR/Ldd9/h6OhEmTJlKF++HG5ubmi1Wp49e8b169e5dPkK9+56U7VqNY4ePfLKvr29vRkzdiwb1q/HwdmVlr2HUrlBUxycZef0d7V9xYLkJMGtzleYWDskvaBQgl6HLjER5f+NAIUE+LFo/DAuHN2ffOzr6sWIi4lGqTbC0q0wkU/volAoMLKwxaXCF3g0SFlV6HVM7V3IXbMt97fNB5LKAL+uupf/s8cAmOcpmeK4c40ePNr8A4ObVic00B+APM3HYuFeOlUfWZU2OmlNQNXeP733uUEPbxMV6EueMjXe2M6jyhdcWvcrndu3Ye7cuWkJU3yEFAolikwuwpCVpv5lN5IoZLDo6GiWLl3K7wsXcf3aVVwKl0JjYUv1Pj/iXu5zNKbvXqJPfJoSYqO5c3ArN3avRaFU0vCHhQT63OTYwp+IDQ9FbWKKytQK5wpNcavfA7XJ+1cvMsuRG/M8Xuxas4SGHXpg5+icAe8kc+QrUpy+P8+mw+Af8L5yHp+bV/G5eZWp02cQGZ50c2RpY0ue/IXxLFOFJ0+f4uvnl6qfCxcu0L//AM6dO4ttDid6/DCZ6k1ap0t1p09JXEwMq2b8hMrEnKqTdv4nif0n+f3n2INb19m/aSVHtm1Em5CAZZ5ClOg9nfPTexLzz0Lkkv1mY523aNrjCQ9KThKsbO0xs3j9NIQDm5NKqxrbplxUa54nqZx1aKA/CrUxeZqPwzxX9too09guF5EPzhHy9D6OBYq/0zneB7dwasmE5K/zlKlJjYFTXnvTp1AoMHdwYcOGjfz000/Y2WXt0UohRGqSKGQQrVbLlClTGDNmDImJidjnyUfD0QtwLVpO5mCKdxbm+5jtY7sRExaMysiExPgY1vapD4DG0p6ivWZhnbfYW3p5N57Nh3BpZjf+XrUoW44q/Je1vQNla9WnbK2k75dOpyNRm4BKpUahVCb/HCbEx3Fg82qqVq1GXHwcsbFxFCpYgB07d5LDNQ/tB42i9pcdZXpRGmlMkhYL67TxqUa6FEo1+sQENv8+C1Nzc/6YlrSg1jpfcQq1GYq5U9LGcaUHz+fBrqW4VGiEVe4CHxSPSmOcvBfFD4s2oFK//s/gy5GGhIiA5IpFL5nn/oyoJ1dxrNwh2yUJAKauheACnF4yASNjEzwqN3xj+/8mCQCPzx/i4obfKN329UU4in/Zm32/9KVAgYLcunWTHDlypEv8QojMIYlCOtPr9fTp04elS5cSHx+Pe9laFG/aBcf86XMzJz4NgT638L97jas7VxIXFZGUELgX4/rioYTdu4iJnSslh/6RbjX0Y0P9uLFwECq1ikKlyqdLn1mNUqlE+YrN4Oq364qZhSXBAX5YaIxJCPRn48aNlK1Vn/4Tf5MEIY30ej2Xjh3g+pnjSQd0uhSv63Q69DotABvnp9xMr2S/2Sn+b2ssbCjYaki6xKU2scC+SGUCrx9n1YzxjJz/+n0ROgwZw8Wj+3mwdiiFB2xBofr3T2aeluPRRoVgZJE9q1tZuJXCoXxbAs+s4+butW9NFK5s+R2AnTt3UqdOHZ48eUK+fPm4vn0Flo65yF+jGUCq0QXXYhWo0vsnjs//gSlTpjB16tQMeT8i+1CoVCmmGmbWNUXaSKLwgXQ6HU+ePCEsLIzjx4/Tt29SLXprFzdqdvqWPKWqGjhCkZ3otAmcW/cbV7f/AShQG5uQr/m3WLsnJZpeXSZyb/N0clZrna4bbV2f14/4qDDGLtmEV5m377/wMcmTv3CKERS9Xs/dqxfwLFoy0/+YfSz0ej2LfhrOgc2rk7+HtgVSzt9XKpWU6DMTc2c34sKC8L90iITocFwrNsrwTeSKdh3H4W9rc+XkEeJjY9C8poiEi9u/i5j/P0mApDnP2TVJgKRSso6V2hN4dgNBPjfRauNRv6ZSWmTAc6JDAunVqxcNGyYlFB4eHpQtV45zZ89yasmE5NEG2zwFcCpUktt716NUqXEpUi65n65d320diRAi65BE4QPVqVuXgwcOJH/tWqQM+as1pkANWaAs3k90SAD7Zw7D784VbAtXokCbkanWGyjVGgq0+T7dr62xcSIuPIi42Jh07zu7USgUFChextBhZGu6xMTk+f3Vph1Ap01AqU69tsOuQCkAjK3sP3hK0ftQqjW4VPyCF6d28m2LWngU+Qy1WoPf00dY2zvQrFs/nHK780u/jgCYOmdebJnNpXYfXuz7jc0DGtFmXupSsQmxMWwe1BiAtm3bpnjt7Jkz3Lp1iwULFuDj48OOHTsIeexNyGNvAHSJWp5dPQnAt99+i5eXVwa/G5EdyIZr2Yvso/ABfH19cXFxIV/lBuQuUYkcnkWxcXU3dFgiGwp6eIe/f/6G+Ogo3Bv1xqVis0y9vi4+ngvTvoK4KH5csRWPwjJVTnyYrlUKEx0RTv4WA8hd/UtDh5NKfGQoJ8c0R5eYiNrUAn1CHInaV29U6NltIRrr7LvA/218Vg0kNuABLkXLUff7+cnH9Xo9xxeMxef4Try8vDh27NhbFyT7+/tjbm7OgwcP8PT0pEePHpQoUYIhQ4bI+rxP3Mt9FB7NGoKVaebuoxAeE4fboBmyj0IaSKKQBjNnzuSnnycQEhyEQqGg+eR12Lt9vE+cRMYKeniHHT9+jS5RR9FvfsXcxcMgccRHhnB5WidMTIyZuOZvHHPmNkgcIvvT6/WM7tCYe9cvYZojFxVHv34dgCHFBL1AaaTB2CppCpFOq+XFuV1E+z7iyeENALi3nYKZSyFDhpnh4sP9ubfkawDqjJiLa7EK6HU6Lqz7lRs7VzJ9+nSGDEmfNSLi0yWJQvYkU4/+IzAwkPnz53Py5Cny5fOgcePG3Lt3j4IFC1K6dGkOHz7MkCFDyF2yCsVaf06u4pUwt3M0dNgim4qNDGPXxD7oEnUUH7AIE3sXg8WisbClSO85XP/tG8Z2acY3P86geKXqBotHZF/7Nv7BveuXAPDqMCrDrvNw3yrUphbkrNw0TU+rTf/z86ZUq1MkCTkqffXRJwkAGitHjKwcSQj3R2VkjC5Ry6Z+9YkJD6FZs+aSJIh0JVOPshdJFP7Pjh07aNw4aS6mxtSC3bt3vXaTGK+6rWWhsvgger2e4wt/IjYijGJ9fjNokvCSuZM7hbpPxXvlGCb2bk+parX58psh5CvybnXWAe5du0RMdCSWNnZY2dhhZecgew98YoqW+3d3Yt/z+7B2T7/yodqYSHx2LibY+zzRfkkbojmVrImRufUH963T6ZKTBI1tTnKUb/PBfb6UGBuB0sgEhSrpZyH62U18D/1O7qajMbI0bMnQyEeXSAj3x9TGAccCxdkz8RtiwkNo27Yta9euNWhsQgjDkqlH/yd3HjdComKo3H0kbmVqEPTIm+iQAMztnfG9fRFtbAyWjjlx8CiMZQ7Xt3coxBtc37WWU8unkLNaa9wbfmPocFLQ6XT4/PUrAef/RqGABfsvYWWbem7yU5+7nPj7T9QaDRpjE0L8fdm5alGKNpY2doxcsEbWPXxi/J89oX/DCgDUmHk43SoZ3ftrPo8P/nPzqlCBPhG7QuUo0nksRmaWH9T381M7ub1uMgD5eyz74KpGCRGBBF/ZSdC5zSmOK40t0MVFAlljR+ebM19dfCMhIQH1G/aZEOJ9vJx69GTuMINMPcrdd4pMPUoD+Q3wjx07dvD0yWNKt/oGt3+2pbd3K5C89sAudz4DRic+RkGP7qA2Ns1ySQIkla70bD4Isxy5ebBjLhGhwdy5fI5b50/RvMcAzh7Yxe1LZ1EqVRzetgG12gidLhH0eizdipCnTlfiQv2IDfbF98QmZn7bgykb92NqbmHotyYyiWPO3FSq35STu//izvqpFG43/IP7DLl76d8kAbCtO4bIS+sIvn2WCzN6Ufa7JaiM077vxfPTOwBwrTvwg5MEbXQYdxd3e+VrL5MEgMd//kjedtMxdc7/QddLK32iNvlzIyMjEhISKFeuHOvWrZMkQQjx8ScK58+f5/z58/Ts2fONT7Smz5gBQLFGHTMrNPEJ0ev1xEaEEPb8EZEBL4iNDOXppROoLd5cQcTQVKZJT2jDggKYNijppue/IwbGVnaUGbnptX1Y5S3O7WXD2LlyEV9+MzjjghVZjqNr0oL4F6f/Jl+jnmgsbd9yxqvp9Xpig19w6beBycdUZkl9WZRsS9zTi0Tf+IsTY1uiVGuwzlsUhdqIyGf3KNJpDJa53u0m3K1OB64t+p7ne2fje2QJtl61cKrx9XvHGxf8FN/DC5O/TkxMRKlUEhMTg6mpKRERESiVSrZv3067du14sPZbclRsj0PZlslTkzJaQkQAYXeO439sefKxrVu3Ju+TIERGkTUK2ctHkyjodLoUiUBUVBQzZsxgzJgxANjZ2dG6detXnvvixQuOHD6Mg3tBjP5Tt16ItHp65RR3j+0k9JkPYc8fkRAbnfyaQqFEaaQhf5sBBozw7VTGJgD81DPlXG0zJ3es8pUk+PpRCned/MY+zJ3cUShVhAcHZlicImvye/bon8/0HB/dlBrT96N8zaZerxL5/D7em2YRev9K8jHz4m3QOKesx2+cqxTo9cQ9vUCiQkHA1aPJrwXdPP3OiUKOopUpN2IFF3/thzY6gqBL29Dr9WhsXVGb22LpWQGF4s03HLr4GB79+SPacD++++47hg8fnvy3yfSfXb4tLZMS8LZt27JlyxY2btxIwKk1aGxcsC6UsQUEtFEhBF3aRti13WhjowCYMmUKQ4cOlfKlQohUPopEYdu2bbRq3ZrGjRrTrFlTQkJCmDFzFg8f+JC/WiPuHt3BpF9+oV69elhb/7vgzdfXFycnJ4YNG4aJhRXVev9owHchPjZX/lrGi1sXMbZ1xjxPEcyd82KRpwhW7sXQWNgYOrx3YpGrMGoTc0wc8+DRuD/mLh483LUI5wpNMMuRm3xN+r+1jwc756HTJVK7lYzWfQpuXTzD9CE9iI4II1GbNK1FoVSj12mJ9n+Kheu7l/89O6U76HUoNOagVGNRoi1q61evDzPOXRrj3KUJP/Xvk3zb/KVwq9PhveK3cMlLtUk78dm5hId7VxB8efu//ZX4ApeavV57bnyYL/eW9gTAxtaWKVOmvPV6GzZsIDo6GnNzc57tmo5VgSoolOm/I7hOm0DgmXUEnt0IwDfffEOdOnXImzcvJUuWTPfrCSE+Dtk+UYiJiWHUqFHEx8WxefMmNm9OmgJhamVLi8nrsHcvSLjvEy5fusSLFy9YvHgxM2bMIG/evJw4cSK5n8J1WmHvXtBQb0N8hBLiYjC2dab0dysNHUqamdjkoPy47SmOeTTu+159JMbHojE2IU/+j7/M5KciLCiQrUt/4+b5U7x4/BC3/IUYu3gja3/7hR1/LAK9DgD7EnWxL16XsLtn8Du5kauLRpCjeHXyNen9ToubVRpjEuNjMXaviGned6syp9CYA1B14g6MzNO+aNHji+7Ye5Xn6fGtJMZFE3jtOOF3jr0xUXiw5lsAypevQOL/zf1/m8DA/xtt0+uA9E0U4oKf4ntgLlFPbwDQsVMn5s+f/5azhMgYCqUi86ceKWW0LK2ydaLg7e1N165duX79OgB5SlWlQqdvURubYmxhjVqTtKreJmde/LyvULhw4eRzY9XmyZ8XadCOcu2z9hQQkb3odIlEBrxAbWXYsodZgS4h1tAhiDSKj41l9awJnDnwNyWr1qJNn+9Qq43oXbcsidp4NFYOKEys8L5ynq/KeYBej7VnWdybfYfa7N+bdNMcefA7uZHYYF+eHFqPxsoet1pt33r98t//wclxrYj13o+RXV7U1rneeo7KyhltoDfXV4wjf7N+7zWC8V/WeYtinbcoAAcHViMxJhx9ohaFKvWfzshHl0iMjcDNzY3Tp0+913Xq168PgE2R2um+RiHc+wQvDszFxsKENX/9Rc2aNZOnPgkhxNtk20Th+vXrNGj4Bc+ePaVwnVbkLFaeHPmKYOHgnKptwVrNeXrlBBaOubDM4UrlbsPRmFmSEBtNQkwUZrZyMyfS180964kJC8KzTndDh2Iw2vhYrs3tQ7TfQwqXrmDocMR70ul0fNeqNr6PH2BkYcfBzWs4vHUDNvYOJGrjyd9hIlYepdAnagm8sg+9Nh5TJw8s8hRJnsevjYnA9/g6/E6lLA/qVLLmO8WQEBmK0kiDLiGeeN9b75QomOX/nDifY4TcOc/ZyV1AoaBkv9nYepZ4329BCha5ChD51Jtnu2eQs+HQ5PeoS4hNmvN/4wAAR44cea9+586dy61btzB2cMelztun8r0rvV5PwKm1BJ5ZR4kSJZk3by4VK1ZMt/6FSCuFUokincolv881Rdpkm0Rh7969LFu2jCtXr/Hk8SMUCiUamxy0nrkVK+fcbzzXqcBntJ+/N9VxIxMzWbws0p1er+fCxt8xzZEHpzL1DR2OwdzfPI0Y/0d0GjqWem27Gjoc8Z62LJqN7+MHOFX8kpy1uxMf6sujHbMJeXwD5yptsfIoBYBCpSZHqQapztclxHJ9TlcSYyOT2+Ws0gz3up3euEZHr9fz/NQOHu5ZQVyoPwBKU1tM8n/+2nN02njQxhD34jqxPkeB/9seSK/n0pwB5G3Qjbz1u7z39+GlQm2HcX7a14R7H8e+dHMSIgOJfHCB0OtJf1uq16hB3z6zcXNze+c+nz9/Tr9+/QDI3Xhkui4mvvVrC9Al0qpVKxYuXIiNjU269S2E+HRki0Rhy5YttGzZEgC3sjUxtovH3r0g5ToMxtQ6a5eXFJ+emLAg4qPCyVnmC0OHYhBh9y9zd9MUEsIDKFq+Kl907GnokMR7io2O5M/FczBz9iRnrS4oFAqMbV3I32ES6BJfOfXmv54fWZOcJBRs/S05Kzd9p2s/2LWUh3tWoFAbo3bIj8rcHhPPmq9d0xBxcTXaAO9Ux03y1UDj5EX4yXnJ/bpWaoyxVdr2R7DKXYCclZvy7MRfPFibtBbB2cWVLl26MHbsWNzd3d+rv6NHj1K9enVURsbkbv4jGpvUo+EfRJcIJC2WFkKItMoWYzHff/89AF9O30zdoTNoMXUD1fv+JEmCyJJCnz4AwCrvp7MTsU6n49YfYzgxohbXFw0hLsSXyg2aMWDSb4YOTaTB7OF90SbE49ZkSIqkQKFQvFOSAGBs+++N792tc7m6aCQh/1fm9HX8LuwHwKzYl1iW7oBZoQYo1SavbZ8Y/gKA/M37k7vmv2V8TT1rEnU15f4eCZFh7xT7f+kStdzbtoDgmyeTj82fP58Xz5+xbNmy90oSAgMDGT9+PNWrJ5VBdW83HbOcXm85693Fh/risyJpE0dJEkRWpFCqDPLxrrRaLaNHjyZv3ryYmpri4eHB+PHj0el0yW30ej3jxo3D1dUVU1NTatSowY0bN1L0ExcXR//+/XFwcMDc3JwmTZrw9OnTFG1CQkLo2LEj1tbWWFtb07FjR0JDQz/o+5vesvyIwvHjx/H29sarXhtsc6V9UZoQmSX4yV1QKLBy/8zQoWQenZaQWydTHLp57hR+Tx9hYW2DUpX+5R5Fxrhz6RyXjh/EoVQDzJzT/js3R+mGmOcsxJPd84h8fJ3A68cJvH6ckgPmYJuv+GvPy9ugGzdX/kT0ze1oHL9963WMc5Uh9v4h7v45hxJ9ZvDk0HoAtKFPURgl7VtQZuhiFArStLBZGxfN1YXfE3rvEk2bNaNL5840bNgQjebd94N46fr165QvX4Ho6CjM3UqSo0I7jO3zvFcfuoQ4QE+svw+B5zYRF/AAjakFChNLdCiJfnIVgJkzZyaPxAsh3t3kyZNZsGABK1asoEiRIpw/f56uXbtibW3NwIFJmz5OmTKFGTNmsHz5cgoUKMDPP/9MnTp1uHPnTnKxgEGDBrF9+3bWrVuHvb093377LY0aNeLChQuo/vmb2L59e54+fcru3bsB6NmzJx07dmT79u2vDs4AsnyicPv2bQDUxqYGjkSId+N7+zJqE3PUn9D6F6VaQ9kftqDX6VCbWBBy5zT31k9gdMfGuLh58EXHnpStWQ8bB0dDhyr+Iz42FqVSifqfG99lk39AaWxOzlofvq7EzNmDgl2mEe17n2cHlxN+7xz+Fw+8MVF4tH81AErjd/v5MfWsQXzAbXThL3h24q/k4yqLHGhylUYb8ogbf/xIxVGr0/Qezv3ShZhgXzp27MTSpUtQq9P+Z3Py5MlER0eRp8WPWLi9+94Fer0ebUQgUU+u8nzv7FSvd2zdlOfPnyffbAwZMoRBgwalOU4hMpRSlfSR2dd8R6dOnaJp06Z88UXS9GF3d3fWrl3L+fPngaSfx1mzZjFq1ChatGgBwIoVK3BycmLNmjX06tWLsLAwlixZwsqVK6lduzYAq1atInfu3Ozfv5969epx69Ytdu/ezenTpylfvjwAixYtomLFity5c4eCBbNGyf4sP/Xoq6++olixYlzdttzQoQjxVnqdjmdXT2Pq+O4LGj8WRmZWaCxsUKrV2BepQunRf+JW/2uCAgJYPOF7fujUhNjo6Ld3JDJUbHQkF47sR6fTsfjnEXQsn4+vyualc6VCjO7YmAe3rmGR2wu1afqV0DRzzodrjaQN9wKuHSc+IuT1bXPkBCAx3JeYe4fRRgURfnIB0bd3EX33ANqQxwDE3DtM6MFfCNkzFt0/048CriRVHDLxrIVCbYzGuUhSW/8nnJvRi7tb56L9Z93Eu4h8fp+YYF/Wr1/PH3+sSHOSkJCQwMyZM7lyJWnqlcbm1ZvGvUrY7SPcmtWUu0u6JycJderUYeLEidy8eROdTseSJUvYtWsXiYmJbN68mfHjx6cpTiE+duHh4Sk+4uLiUrWpUqUKBw4cwNs7ae3TlStXOH78OA0bNgTgwYMH+Pr6Urdu3eRzjI2NqV69OidPJo2sX7hwgYSEhBRtXF1dKVq0aHKbU6dOYW1tnZwkAFSoUAFra+vkNllBlh1R0Ov1+Pn5YWlpybVr15KPyRbzIisL9LlFfHQEOYvXMnQoBqfWmJCrRnty1WjPoz1LeXpoFTv++J1aLdph55jOCzfFWz25d4f5YwZz/0bSzWrzrwdw4cg+AHKUaUSMnw93r1/BxCEXThW/TPfrmznnwzJvCSIeXObinP5UGLnqle0KfDmYgKvHAIi9f4jY+4cASIxISgbifI6CQpm8qdtLSjN7NM5FSIwKROOctPeBQqlGbeeBNtiHiEe3iHh0i/iwIIp0HvNOMT8/tQM7BweaNWuWlrecrHPnLqxduwYAhwpt0Vg7vbG9Xq8n9Noe/E/8kbQgXKFEpVLy0/jxdO/eHUfHV4/MKZXK5CecQojUcudOWSVz7NixjBs3LsWx4cOHExYWRqFChVCpVCQmJjJhwgTatWsHgK+vLwBOTil/jp2cnHj06FFyG41Gg62tbao2L8/39fV95c+yo6NjcpusIEsmComJiXz33XfMnDkz+VjdYbMlSRBZml6n48zqWSjVRjiWSV0u8lOWs2pr/E5tYeP8aWycP42R89cQ+OIpDi65KF6peoZdNy4mhutnj3Px6H4uHNlHeEgw7oWK0LhTLyrWa5Lu19PGx6PVxvP47h08i5V8p92H09PJ3X/x6/f9UBtpGDz1d6xs7XDK7Y5SoWBUh8bEJyQkt927fgVREWGYOecjT8OkEp16XeJ7Lfp7HwqlCqcKLYl4cBlbz9dPuzG2dqDmzEMkJsRx+bfBRAc+w9zZHeey9Yh64cOz41vR/1PRp+KYddz44yfCH95AaWyJ6StKqFqU7kD0ze0kBN5HHxeOfdFK7xxzQkQwukTdB/87vkwSAOxLNUOv1xFx/wzaqFDMcnph4pA0AqnX6wg6t5nw+2eI9U16mrlhwwZ6ffMN0TGxWFhYvDZJECLbUCqTPjL7msCTJ0+wsvp3M0hjY+NUTdevX8+qVatYs2YNRYoU4fLlywwaNAhXV1c6d+6c3O6/96Tv8jD7v21e1T6rPRTPkonC/PnzmTlzJuZ2jpRtPwAbF3dyeBYxdFhCvJZOl8jpFdN4cfM8bvV7oNa8vkrLp0htZkG5cTu49ccYgm8eZ2Lv9kDSE9DV5x+m+2LnB7eus/63yVw7cwxtQgJqjTEa+1xYF/Dk/vUT7F63PF0ThWc+d/F79pi5owcSGZo0raZS/SYMnDw/3a7xLoL9fdHrdCTExTJlQNIfNIVSib2TK/Hx8eRuOYnHG4YCJCcJ+dqMSz4/o5KElx7vmgsoyP/l4De2UyhVqI3NKPPt76ley1W1BU+PbcHcyR1Te1fy1u/KlQVD0YY8fG1fZoUbErp/AgARj2/jXLr2W2N9fmoHfpcO4eae961t32b06NH8/PPPANyZl3JHagv30uRpPhZtTDjeCzokH+/evTtz5szB1NSUL7/8kvHjx7/XHg1CiNSsrKxSJAqv8t133zFixAjatk36WS1WrBiPHj1i0qRJdO7cGWfnpBFxX19fXFxcks/z9/dPHmVwdnYmPj6ekJCQFKMK/v7+VKpUKbmNn59fqusHBASkGq0wpCyZKLwsG6dQqfGs3EB21BNZmi5Ry55f+vP06mnsP6tBrhrtUr6u1aL8gAWQ6Sns4TUCLh3AxNYJS7eiWGdyCdfCncYTG+pHTMATgq4fx+/MNuLj4jAxe7eFqzqdjmunj7F3/XJ0Oh1fDR5NLo/8Kdqc2PUX88cOQa8Hy3xlcK7QGJsC5ZKfCp8d1wi9TpduT21+6deJS8cOJH+do1RdAi7uxfvqxQ/u+12N6/4lt86fwjlP0k2tUmOG2swGFAriQ54R+OIp9uXbYeKYD/cO83m4qjcAeRoNQmOdOTvT67TxxIcmDad/yBN6M8fcFGg5MPlr2/wl3n6S0gilqR26mGCeHN5A0M3TVBj16qlPusREnp3cxt1NM7GysmL/vr0ftIAZkhKFKlWqUL9+yg0YLT0r4Vp3APGhL7i3rBcAhb288Ll/nyVLlrBkyRIOHjxIzZo1GTt27AfFIERWoVCpUGRyJbz3uV50dHSq31EqlSq5PGrevHlxdnZm3759lCyZNDoaHx/PkSNHmDx5MgClS5fGyMiIffv20bp1awBevHjB9evXmTJlCgAVK1YkLCyMs2fPUq5cOQDOnDlDWFhYcjKRFWSNu5f/KFq0KN27d2fJkiVc2rKIUl/2MnRIQrxWXFQ4T6+extg+Jyq1MTeWDifq+T20MRFJUyT0euy8KlO400+GDpVby0clb4KVRIGlmxde3ae+8yhI1AsfTJ3c03yzZ2LjhImNE5FPkiqahQT44uL2+rKVj+/e5tqZY9w8f4qb504SHRmB2tgM9IlcalkLV/d85CtSnAKflebxvdvsXb8CE1snivWdh8bCNlV/juWbcufIWk7s2kqVhs3T9B5eGtG2AQ9uJZWj9Oo6GaWRBqu8n5EYE0ng7VPERkdiYmbxQdd4F7fOnwLA9/EDbIo1xLZUM4wskxKA0Ou7AQVWhWoAoLFxRmViicrEDHPX/K/p8cPp9ToCL+5Gr0vEsWxj/E5tTnohnYfUlep/y5TqExNQqIxSN0qMRxcTnPxltP9jfM/txblsXWICn3Pqp7YYW9mjMDImNug5AOXLV+DIkcOvnJrwvoyNjalXrx6JiYmULFmSq1evYparGLkbj0CfmMDjPbOwd8iBWqXi1s2bjBgxgmnTp6NNSEjxxPHhw4c4OztjYiIjlkJklMaNGzNhwgTy5MlDkSJFuHTpEjNmzKBbt25A0nShQYMGMXHiRPLnz0/+/PmZOHEiZmZmtG+fNFpubW1N9+7d+fbbb7G3t8fOzo6hQ4dSrFix5CpIhQsXpn79+vTo0YPff08aPe3ZsyeNGjXKMhWPIIsmCgqFgkWLFnH+/HkubFyAub0TBWs2M3RYQrxSQnQUAHFBz/APeoZCZYSpQy5M7FzQWOcg6NoRQm6fRhsbnSVKpqpMzCn69XSiXtznxem/iHh0g7Pjm+JatRXu9b5O0fb5yT/xP78Hh8+q4VqtLdfm9SPy6W1UxmaUGLAIE3uX11zl7RKikja/OvzXetr2H8Gti2ewc3TGObd7cptA32cMa1UbvV6PxswKE6e8uFevgkvFZiTGR/Ng+28EP7uL/75dHN2xGQXgULwW+duMfG0i496gB/4Xd7Nl4awPShRO7v6LB7euYmRhi1PZhtgUKJP8mrVnaYJvnaR7jeLM33MOK9uM2RwyOjKcYa3+raqRt9PvGFmlnMNuU/Tfp9g6nY7Ak8tJjI0gR+mMW0cTHxbA7WVDSAgPAODJrrnJr33Wc3K6Xuvc9H93/tbFR6IyTZ0cRl7ZmPz5gwcPGDHie/7cPIunx7YQ/ugmAHHhQeT18CBUb08+Dw+OHj2Spr0SXmf79u2sXr2Gq1eTEsvop9fwXtgFbVRSAtO+e3caNWrEmDFjGDlyJOPHj2fIkCEULlwYgHv37pE/f1Jip9fr0y0uITJdFi+POmfOHH744Qf69OmDv78/rq6u9OrVizFj/i2CMGzYMGJiYujTpw8hISGUL1+evXv3Ju+hAEl7majValq3bk1MTAyff/45y5cvT95DAWD16tUMGDAguTpSkyZN+O23rLVRqUKfhX/jHDt2jGrVqgHQfe15lJn9H0uIV9Dr9Ty/fhbvw9vwvX2JyMCkaiwKlZrP+vyGuWv+FFNa/C/t5+76ieT6vBNKlZr4yBD0CXGozW3IU7tLpk1L0mnjOTW6Po5lGpL/y6HJx0PvX+LRroVEPr2DkaUd+ZoPxt6rMjqdjjPjGqGLj03Rj5V7MSKfeaNLiENjZU/xAQtf+eT+bZ6f2MKD7Sl/IVZt1JJ+E35N/lqv17Ny+nh2rlyITYGyFOn2+pvM+MgQtDGRmOXI/do2AFEv7nNtwUAKFy/JD4vStnNtbEw0feqWJSo8lHJj/kJtmnLUQK9L5NHuRTw/tpHB036nQp1GabrO2wxqUpUXj3ywr9Ae6yJ1UZtav7H9iz0ziLh7DBP7XBTsNjNdS6C+pNclcumX5ui18VjnK45el0jUcx+MbXKQv3l/7AuXS7dr6bTxHP426emcefFWydWO/itkz7/TdnQ6HSEhIfTp04f169enjD0D/hwuXLiQefPmJZdGfZUuXbowd+5czP5vCl5oaCi2traMGTOGH3/8kZCQEOzs7Bg3bpxMQxLZUnh4ONbW1viunYyVWeaOioVHx+LcLqma0dvWKIiUsuSIwksrVqwAwC6XBwqFrFMQhpEQG8PzG+cIvH+DxxePEeb7mISYKFQaE4xtnXEq3xibfKWw8vjslTfMxlYOgIKnB/5I9dqzw2vwbDkUp7INM/6NKJN+3ENun0px2CZfSax6z+HxvmX4nt7O7T/GoLG0xdTRDV18LM4VmhIX/ILogMdY5i5MgXajiQt+weU53xAfHoTPtjkUav9u5Sb/n2vlFpjYuXBv0xSUxmbEBb/g2I7N1G7ZgUKlkm4mFQoFHb8dw4Nb17h5/hQvTm3DpeKrFyFrLGzfmrCE3rvIzaXDUKlU1Gze7o1t32TZxFFEhYeSu07XVEkCQOSzu/ie2Y7G1IwSlWuk+Tqvo9PpWPbLaF488gGFAvsyrd7tvMR4ADxaj8mQJOElfaIWtbkVJfvNztDKTxHP7gOgtnV7bZIQ++hMiq8TExOxs7Nj3bp1tGzZMnn+8IULFzIkxl69kqbOKoxMsC/ZmKhHF4nxS4q7evUadO3aJUUllZdsbGyIjY1NHtWwtbWVkQQhRKbL0omCp6cnANX6jEehUKCNi0EbF4uJ1fs/vRTiXem0CUQG+xPoc5PHF47y8OxBEmKjUahUqE2tMbbPjWO+kuSp0/WdRgOs85Wg7KiNRPs/wtTeFZWpJbq4GM5NTLq5e3p4LUE3ThAX4os2NhLTHLkp+vW0dH9fSqUSlakl2uhwdNoElOp/53IrVWrc6/cg9+ed8D/3N4/2LiXs/iXsClfErf7XqE3MU/RlYu9KmeFruLtxMkFXD3P67nmKdJuCZe5C7xWTXeGKlPvhTwDub5uD78k/Gdu1Od2+n0DVRi0xs7Bk2/L53Dx/CjNHN3KU/LD9Ke5tnIy1nQPTNh/A0ibt04GO7dqKiUNOXKuk3m8gPjyI23+MxtjIiOl/Hkr3NQo6nY5RHRrh889+CLmavvvmWglhSfPdM3wBs16HNiqcqOf3sMxVIMMuY5k7aR6vNuQR2pDHqCydUfz/mgVtPDG3/07++sGDBykWJrdq1QqdTodOp0sxHSA92draEhISgj4hlsCzG1GpVNSpU4eGDRu+dffk9FgfIUSWo1QaYOqRPGxOqyydKLRv357vv/+ex5eOY2ptz/qBTdBpE1AoVag0GsxsHMhbvg6fNemEicWbh9yFeJMTS3/hxc0LmFjaEOhzk4TYpB2EVRoTNNaOeLToil3Rqml+OqqxtENj+X83psZmOJVrhN/ZHcQGPSM+LABjW2cUCiVh9y4S5nMFa4/i6fHWkiVEh2OZpzChd84Scucs9kUqp2qjMjLGpVJz7ItWI+LxDey8qry26pja1IJCHX7E/8Ie7m+dybX5/Sk+cBHmTu5pii9fk/7o4mIIuX2apZNGsXTSKEzMzImNjsLSrQif9Z6Tpn5ferRnKXFhAXT/edYHJQkn92wjMSEez3o9UL1iAfi9zdPQxkQwdvmf2DulfQ3H6wxsVAn/Z0+wK9sam2INkqobvYMYX2/igx+Ro2wTVBrTdI8LID48kLtrRgOg1JhkaJIAScmvmbM70b4PiTi7BAAj56JoHAuhcSlGYqR/cltLS0vc3d1T9aFQKDIsSQD49ttvGT16NGXLlmXZsmXkypULa2v5eyWEyB6y9BoFgIKFCuF9584b26iNTXArU4MafX9CqcrSuY/IYmIjwzi+8CcenEkqb6mxzoGJvSs2nqWxzOOFjWepDL1+QlQYiXExGNs6oVAoSIyP4ez45ljk8aJYzxnpdp0bS4YRevd8UsUZvZ68jfvhWjn9dnB9cWorPn8lrS0oP25H8qJtbWw08WEBmDm9X/335yf/5Mm+pSg15pg5uVG404Q0r+WIeHKbO6vGEBcWiFeZivywaMMHTYf5tnlNnvp4U27sX6hNUo4WJMbHcGZcE8rXbsiQaan3APhQUwd14/yhPQDk77v5vaZkvtg3m4g7hyncax5mTq+vMvUh7m8YT+jtkxhZ2FDxh3WZtnj/2Ym/8Nm5OHmB/Kv4+fkZZLMyvV5PVFQUFhYZX/1KiKzs5RoFv43TsTLLmIcVr712dAxOrb6VNQppkOXvqv/csoX6DRrw5PFjAMqXL8+qVavw8fFhxYoVbNmyhdjYWO6fSCoBWGvARMMGLLK8Z9fOcPDX70mIjSYxPg4AU0c3inw99Z/1BJnHyNwaI/N/ny6qNKY4lKhN4OV96LTxKNUa4iNDCLl9GiMLW6w8Srz3Zm7e6yclJQmAmaM7Zs55cSiWvrshu1Rshi4hjod//875Sa0p2GEctvnLcGFyO7QxEZg65CZ/m+/feWqSa6XmuFb6sNKl2thIvNdNJOzuOSysrOk56Tcq1W/6wXPmzSwtUaqNUyUJAPFhgaDXkb/Y63ceTquXSYJZnpI4VOz4XkmCNjaCSJ/T6R7Tf4XeOY2JvSuVxqzL8Gv9v5yVm5KzclOendrOw90riAtNGkkwd/EgLtSfAh5uODhk7s/2SwqFQpIEIUS2leUTBS8vL7b99Rc7d+7Ey8uLhg0bYmxsjKenJ3Xr1k2uBAEQ9PDNIw9CAPjduUxseAhKY1MschfG2rMkeT7vlKIeuyE5l/sC//N/c2p0fVTGZugS4pL2YwBQKHFv+A0u5ZsQHxXMreWjiA16hnnO/Hh1nfTKm9eXSULBr8bhUKxahsWds1obzF08ubFsODeXDMPM2QNtTAQAMYFPuDq3DyUGL03z1KT3dWVOb2KDnlGndSfa9R+BuVX6TPcIeP4U01eMkCTGRfN471IUSiVla9V/xZlpt27OZM4f2oOJc0FyNhr1Xjso63Q6nm4ZjT4hFmNblwwbTYgNfg56HRqrjCkF+y5yVmxMzoqNUxwL9r7A5bmDadiwIQqFguHDh1OjRo0MjSM6OpqaNWtiY2PLnj27M/RaQgiRkbJ8ogBQokQJSpQo8crX/n+xl2eV9P3jLD5OeUpV48LGBejiYoh8covIJ7cIunKY0sNevVNrZrPIWQDLPIVBocTc2QOViTk2nqUJe3CFpwdX8XDnPB7unJfinIhHN7gw+StKf78etcaEqBf3ub1yLAq1EQmRIZjnLJChScJLNvlLU3bEenzP7uDZ0dSlRy/P7AYosMhVEJv8Zchdp0uGVcVxKPE5Tw/8QdFyldMtSQAwtbAkws8XvU6XYv3Gw50LCLp+FBc3DxycXNPtegC71ixFbWFPriZj3ytJiPG7x9M/R6HXxuNctR2uNTqla1z/7+XieH2iNsOukRZ2BUrjVOpz9uxJmrJVt27dDE8Uvv/+e27cvEX37t0y9DpCZEsKA+yjoJDy+mmVLRKFNzE1/Xeem1PB9B/uFx8fB4/C1Ow/kUNzRgJgZGqBnqyzVEehUvFZn7mpjtvkL43DZzUIuLiPhKgwdNo48tTthql9Th7tWcLTQ6s5M6YhGuscxIcFoDQyQWOZVCFMGx2eafFrrOzJU7szuaq3w//SXu5vSVprUbD9GO6sGQ/oiXx6m8int3l+fNP/2Dvv+CjKdY9/Z7Zvstn0SkgIvRfpVQULilhBRMUOdrEcxd5Rr70XzlHsXVFQQXrvnRAIgZDey26S7Ttz/9iwEGlJ2BRwvvez52Zm3vLMuuzO732fQmSvs+k44eGA25F03o3kLfmSDYv/Clgdg5K8HAqyM9GGRB1VYThu+JVYD+6kIOsAU4Z0ol3Xnlx+6z1k7NyK027j+oeebpQoOpC6HYetmshhVyE2MAi5YMGryB4Xbc6fRszgU3PlOhma4HC0IVFUZe9hyfRRtD13Mh3GT2vSOeuLJjjU//cDDzzAAw88wGuvvcadd97p/w3Jzs4mMzMTURQZPnx4nVooDaGyspKdO3fy0IMP8MwzzwTAegUFBYWWo9UHM9eH0NBQLBYLKUPOZ/T0wFb9VDhzcVRV4nHa+fvV+7HV2On34OyWNumUqNy/lcy572MrPABA4pgbaTtmCvayPAAMEQktYpezsghHeaE/i5Ps9SLLXvKWf0/2ws8AGPbykoDO6bKWs/2dW3BVW+g5aARPfBIYn/mlc77jo6cfpPttbxwzK5Usy1Rl7yZ7/idYD+6qc02j0zP5vke56Npbj+p3PEoL8vjz6//yx1f/RdToaHv162jN9c+ktO+DqwhpfxYdrql/CtVTwV1TScHyryjZNA+Aoc/8iD4splnmPhYel4Ndnz1F+W5ffEbcRY9RuuZz3JV5/jYp7TtwYH9GnX63TZ3GJx9/1Kg5s7OzSUpKYvbs2cesj6Cg8G/FH8z8y7uEBDVzMHONnZgr7lGCmRvBGSEUpk6dyqxZswC44v++JyKpaVPyKZxZ/DpjMjVV1fR76POWNiUgSB43steDSte8X8SNYctrU7CX5hLeYwQdr3okYFlycld8T9afH3Pjw89x/tU3oApQ9WtrRTm3ndObiO7D6HztMyds63XasGalotIZ8dRYyPrrE+ylOUy440Guuv2Bes33zM1Xkrb5cBBy3IUPY+owpN727vtgAuG9R5N8yf317hMIbAX7SJt1D+FdBtLnjsDXBKkv2z95hLLUtWhDE4gYOoWg5AEASG4HjsK9OIr24a7Moyp9OQDGtv18tRfyd7F//35SUhoWz5GTk8MTTzyBy+Xi66+/btJicwoKpxuKUDg9Oe1djwDefvttv1CwW8pb2BqF0w1BFFuV69GpIqo1cEQxtdZMu/H3kLPoc8p3rWTz/m30vu+/VOxdi7MsnzbnTmm0cAjrOIAsPsbr9QRMJACEhIUjCEKdol7HQ6UzEtZpgP9YFxbD9nem8uOHr7Pst+/pO3w019w3A2Pw8X+0vJ7D/v7asDYEt+vfIHsFlYayrQuI6juWoDYNK4Z3KhhiO2BK6kVF+mZc1ZVoj3D9aQpc1ZXYinMQNTrcNRZkrwdrdhrlaRswdT6bmNH31mkvavQYE3tjTPTtCkWNmoao8WUTc1uLyf7mToYMHUbmgf0YjfX/DM6fP59Va9ZyIGMfU6ZMYezYsYG7SQWFMwRBFI9bn6cp51RoHGfEO2cwGPzp55a//2QLW6NwuiEIAshSS5vxrySs0wB63fkeSWOn4rFXsfnlqznw61vkrfiBDc9fTsXejY0aNyguBa3RxJevP8f9l45kzv/eQ/J6T8nWbauWcNfYQciSF1Pbbo2wqT1dbngR8GVO+vuHz7lpeHfuOL+/v8ryP3HabWhMUcSe/wBtJ76GoGqYAGw78VUEtZasP95p1jgVQRCIP+cGZMnLzk+b9js57dtXWPX4eLa8fRebXruV7R8+yI5PHuHg/NkgqogYNPmkYxwSCQCakGjMPS+muKiQoKAg3nijfvVMrFYrvXv3ZtCA/qg1Gi666CL69OmL3W5v7K0pKCgotDhnhOsR4A88U+sN3PT5mha2RuF0Yu5TN1O4dyv6yDZ0uOo/mJN7trRJ/0psxVnkLf8eBIjuez67Zvncc0StAdnrxhCVSN/p/6v3eG6blbwVP1C2YymOikKGnDeOu2e+g1pTvzS4B1K3M+fT90ndtBZRFLFWlIEsE9rxLDpNfuqYqWhPhOXgTlI/nu4/Du8/AUFUUbbpJ6Ji43jk3c/Zn7qd8OgY5nz6PvFJKSz66SvUIbG0u/6D4w98Esq3zqV09afowuLocc9njR6nociSly0vXAxA8vlTSLm4/rEZ9SVr4Vfsn/cJhoQehPYej+RxotIFAQIqQwgqfQjq4IgGj3vwi6l4qkv9xyf6mXzppZf4+uuvycnNw2qpBMDc40KcJZk4ivYSERHJzTffzP/9nxI/p/Dv5pDrUfGc91vE9Sj6srsU16NGcEYIhcrKSsLCfNld+lx2MwOuuaeFLVI4nSjO2EXq/O84uHEJHqeDmIHj6HB58/p0KxyNJXMHuUu+pHLfZkS1FsnjousNLxLetf4++oc4+Ocn5K/8nu4Dh3H/ax8THBJ6zHYleTksn/cTe7ZsYOf6VSDLmNv3RVD5UuvFDBxHRPfhDZ5f8nhY9+QFAAQl9ydh3OP+a0XLPsay6x+59gXRv8sV1vdSoobd2OA5D8/tIuOjqwHoef/XaE0Nf3BuLHmLP6Vw9Q9ogsMY8eJvARnTmr2Hqrx9aIJCSf3saXQxHYgf9ySiJjAPHracbeTPPRz8/eOPP3LVVVcdt/2hRSqVPoTYsY+gi0z221J9YB2F8/8P8LnI5uXl8corRwuG6upqnE4nERHN999GQaG58QuF3z9qGaEw/nZFKDSCM0IoACQlJZFdW7251yU3MOi66S1rkMJph7Omil9nXENVcR5BcR1oN+5OzO37tLRZ/2pkrxdHZSGiSsOmlycBjc+QlLfyR7Lnf0JUXBtmvP8l8cnt+d/Mx1j91294vV48bjcetxNkGQSRmIHjaHv+TWiMp/6j4igvYMur1wHQ8a5f6qTelL0eqvavxW0tRB0UgagxYGzbG7elCK05tsEpUY9F9s+P4ijYQ59Hfq5dcW8eqrN3sXf2Q0T2HEGvW1885fFqirJZP/M6/7E2LJH48U+jDgpckbec7+4lxqRif0YG6pPEt6SnpzN6zBhyc3JInjLrmLsXttwdFP31Ml63A4D333+fhIQEOnbsCMCKFSu44447ANiyZQt9+yppvhXOTBShcHpyxgiFDRs2MGjQIP/xFa98R0Ry5xa0SOF0pDw7gwWv3Et1aQGiWsOg5/5SMpe0EtK/f4mSrQvRmMLpMfVNjFGJDR6jYt8W0r98AlEAQ3AwlrLSOtfbXngrEd2GowuPQ1QFLgg67cunqNi9mviLZhCcMujkHQJI0dIPsaQuRB+RQPe7/tts80peN7s/vB13VSnDZ85DrdWdvNMJsOaks+396Xjs1QCoTVG0ufJl1MawQJjrJ+erqZw95Czmzz92RWVZlvnmm2949733WL9uHTpzNNFjH0MX3va4Y8qyjOxxUrT4HWoOrDtuO4CePXsSHx/PokWLGDt2LL/99pvyHaRwRuAXCvM+ISQoMBnu6j13jY3ocVMVodAITvusR3l5eWzfvh2n00mPHj3YtcuXu7y6rFARCgoNJrxtB655/0+yt65kwcv3krf8WxLPubalzVIAOk6cgS4sltwlX7L1jRtpN+4u4odd0aAxwjr2o89DX5D26QwsRZkgCKi0RrzOGgBElRZDIwTIyajOSQNBbHaRAFC1fw0gEzNsYrPOm7/0C5zlebQbe/Mpi4TNb92JJfNwXYo2V76CNiIJsR7ZpxqKOjSBZcuWYbfb6xT0PMR3333Hddf5djWiRk7F1OUcRPWJ708QBASNntjzH6QmazOSy4ZKH4Ko0aM2RaE2hmLZNR97fir7S63s3OmrIj1v3jxqamowmUwBv08FBQWF+nDaCoV/7iAciSE0gsQ+w5rZIoUziTa9hhAUHk3u4q9IGDYBURv4BxKFhiEIAknn30REjxFsf2cqmXPfx+Oopu3oKQ0aRxcSSZ/pdVfW1zx2HrLk5eAfHyJqdcQODEwl50PIkgSyhKN4P/ro9gEd+6Rze9wA6MLim21Od3UFRWt/Rh8eR7sLbzylsaw56VgyU9HHdSNi8LWIGgO6yOSA2HksgtoPpzh7uz8t6j9jFA7Fw0UOuwlzjwsbNLYgqghuN/CY10J7X0Jo70sAKF72IdbdC8nPz1dEgoKCQoty2gqFQwFh5rgkItt1obqsiOqSfEbd8Sxx3fsH1G1A4d+HqFLT+9KbWPPZK6TOfoSeU99saZMUagmO70D/Gd+y6eVryFk4G0FUnfKuT1BCR6pz9gIyB+a8RUhSD4wxyQ0epzxtLRk/v4qnxgKAqNERHN8RT00lADXZW5pVKBSv+hTZ4yRmyJWYkno0y5yyLLHrvZtBluk+5dRTo27/6CFAJnrUNLThgd/t+SchXUfjdVRRtvYLACZMmMCoUaNYvny5v40+LJ7gDk2zGOUqz8W6eyEAZWVlxMXVvxK3gsLpgKBS+RNENOecCo3jtH2a7tLFVzzIUpCFpSALfXAIfa6YSt6uDeTtWk/3C6/BGBZVJ2hQQaG+eFwO7JYyAKoO7jpJa4XmxlV1uLCirejgKY/X+y5f+lFbSQ5bX7+BbW/dQuLoG4gZNA6tqf6BspaMzX6RACC5nVhz0vzHQYnNF6ia+eWduC0FmNr1JWHMLc02r+R2IbnsaILMmNudmjipPLATj60KU6dRzSISDhHW9zJCup1HwbzncRSl1xEJgkpL7KUvoDaGBmQuWZZA8pL/x0yCkvpRXRvDcOmll9KjR/OIOwUFBYXjcdoKhaeeegqn00lhYSHr1q1j//79rPviNf/17b/NRq3Tc8Ej7xLfvWHVTBX+vWRvWUnezvXsWfwLHqcdQRQJTujY0mYp/IPy3b5aKSHtetF50uMnaV1/jFGJxAy6hKL1c8lZ/Dk5iz9n8PPzfdWuT4KjvICCNb8C0O2mVwjrfLgq84G571Gw+heqMzcgy14MsU0bP+VxVOO2FACQcuUMBKH5gmFlr8/VSdScWlwCwK7PnkKWvIQPuuaUx2ooKl0QCZe/iOx1I6h12HO2Y0jo3uCid8fD66iiau9yqnbOxWkt8Z2zlaMK8mVOysnJCcg8CgqtDlH0vZp7ToVGcdoKBZ1Ox2uvHRYGH3zwAStWrOCbb75hy5YtLFy4kMcee4zsLSsUoaBQL9KXz2X5B08jqtWIai0J50wm+YLAF4lSOHViBlxM2c4VWDN3sOfrZ+hy7TMBG7vD5ffT4fL72fjS1bgsJez/5XU6Tpxx0n46c7T/b3Wwuc61qH7nU7BmDuWbfqR8048YEnpg6jiCkE4jApL+9J+I2sMZRdRG8wlaBp69nz0IgMbYeN96yeNh6wf347KWET7gajSm6JN3agIEUYUg+lwWjG37nPJ4suSlYP4rSI5qHIV7EQTo1Kkze2uFguyyYy/fBsDZZ599yvMpKCgonCpnTHrUYxEeEUlFuc99JDyxPf0n3U1S/7Nb1iiFVsun1w1C8noZ/NxfiCfJn67Q8nhsVax/7lIAOl/zFJG9zw7o+FV56ex493YA+tz/Gcbo46e/BHBZy9j0ki+zUMdJjxPdZ/Qx2pSz/b07cNU+GAoqDWH9riBi4NUBd5NMf+9yAM566thpPgONZd8GMr57xl8o7qz7P8Sc3L3B4+Qs/4l9v74Lsow+phMJV7x02ruQemyVVO1ZQtm6r+qc37BhAwMG+HaefvzxR7755hvKKyr46MMP6dq1a0uYqqDQZBxKj1qy8IsWSY8add4UJT1qIzijn4YeevABHn/8cS666CJKS8v4+9X76X/1XfS5/JbT/odHIbBIkhev24U6KFQRCacJTuvhGgjBSYF/qCpcMwfwrSrrw2JP2FaWZfZ8+ZTvQBAITuh0zHbakHAGPPY94KvpsO+7Fyjf+D2WnX8SMfhaDLFd0EUmBeweDtnWHN93Gd/67j+sywB63vQsan1wg8co3r6Mfb+8gzY0gciRUzEk9Dgtv6u9jioyP72BkG7nIai0VKXOR5K8GI1GTCYTgiCwYMECevXq5e8zYcIEJkyY0IJWKygoKBzNGe209dhjjyHLMn/88Qfr1q3lsssuY9P371O0d1tLm6bQyhBFFfqQMDw1layecS5b3rgJR0VhS5ulcAKMMcnEDLwYgLTZjwV8/Mhe5wCgNob43U+Ox96vn6E6dw+CWsuwlxbXqxhcWMd+9Lj9bcD3YFm87COyvptOwcK3T914QBvhExxVmdsCMt7JiK2t02AvyWuUSABInf0sAPGXPouxTc/TUiQAVG6fC4B190IsO//gySefICsri5qaGgoLCykoKKgjEhQUFBRaK2e0UDgSQRD46quvEEWRdZ+/dvIOCi2Cw1pBzrbVuB02/zlZltmz+Be2/DyLP56fxrd3XcRnU4bwzR0XsGrWi0heT0DmvvSFL+h2/kQiU7phL84id9l3ARlXoWkQBIEOV/j84W2FmQEfP6zzANTGENzVFWT+8eEJ24Z2OAsA2eNCkqR6z2GMSmTQc38S1fc8vxip2ruM9Pcup2TVbCS3o9H2J145EwB70YFGj9EQ4s+9CQBHWT6lu1Y3uH/lgZ3IkhdDQk/UQfXPNNXa8NgqceRu9x8/9dRTPPPMM7Rte2LXNQWFfwuHYn+a+6XQOM7oGIVjMWnSJL7//ntu+XpDvTKZKDQdHpcTZ7UFlUaLpSCb/av/InP9YmwVJRjMEYQmJFORsx9RrcFWUYIgiAiCgFECEQGbIOGRJSKSOxPZrituh43e428kop0vdW5jVyMd1gq+vO1czB360eNWRVS2dvb99CrFm/7C3L4vPW57PeDjr55xLgBDXvz7hD82G567DI+9iiEzFyE2MsOGq7qCzS9fg+RxARA14mbCaotwNZSqA+sp+PNl2o67j6h+Yxs1RkMpT11B5q+vgCz7YxXihlxM10mPnLSv5HGx7MExAKTc+nWTBHk3B1nf3I27Mt9/7HA40OlOPQOUgsLpzqEYhdLFX7dIjELk6GuVGIVG8K9zxi4sKgLA7bCjC1aEQnMiyzLl2fuwFmZTkbOfzT9+DNTVqcGoSURPYbWVyrTtGGXfw35HQhgsh9VtLsNKykjL2kfZwb0AHFj7N0GRsYiimgGT7yFl0BiEIx7acratYcVHz9Dl3Mvpe9VUxBM8+FVl7Q7YvSs0He3G3UHZzuVY9m9lw4tX0e/+2aiNjXN9ORZRfcdQsnURxZvm+12djoXHXgXA2sfGYErqTsyAi4np37DKvdrgMIa8MB/J5WLtUxfiddQ02m57nq/+hzYkqtFjNJTw7iOpOriN0s1/+s8VrP2DgnV/og2JIG7gBbS78Jaj4oAkjwenpYywTv2pSN9E8dL3ib3goWaz+1RwlmZSk7UZc/cLEbUGv0h49dVXGTdunCISFBT+idAC6VGbMUX0mca/SihIksTyZcsAqMw/SEwnxUe0OZC8Hrb//jkZq/6kMvewG0QwKtQIGFARjpZE9CRRu8rgrd/YI4igvWxErB1nA5UcLC9GkiSWvPUI6b2G0OPia4nvMRCVWkP+rg3YKkrY8vMnZG5YQpveQ9CHhKFSawmJTSSxz1B0plCf3bWrugqtG7U+mEFP/86ax8bgrion49fXApsu9cqHKNm2mOr8DGJO0K7TNU+S/u3zAFRlpVKVlYq9+CDJF93e4DlFrRa1wYQtazNyYzMiib6vd625+VKLyrLsFwndpv+EozSbih3zqcneibuqjKyFX5O95Aeieo9EcjnRhoRjatuZvd+9CoCg1iKotVTvX4Ps9SCoDv9ESR4ngkrTrDUhTkbJ8o+xpC4AwHZwE7FjfWl0hw8fwUMPnR5CR0FBQeFE/KuEwtq1a/1/hyW2b0FL/l0c3LCUTd+9hw6RtuhJxEAKRowB+vjFc9hF4TyioNZFfCHFZO7aQO6OtWj0Rozh0VjyD6LSGYjoOYqKtLWkzv8WyXM4xiG8bUeM4bUrsLKE01qKLiQyIHYqNB35q34CQBcWQ8ql9wV07NRPZ4As47IUn7BdZK+ziex1NgC24iy2vXkzeSt+aJRQAAhK6IglYwuy24HQCDcc2W0HQNRoGzV/YzhUbM03rx5jXCeMcb4MULIsY927ipL1P1C8ZfHhTr7aeYR0HIp1X+2BICDLEgLgqSnHlrOd4iXvYu51MVHDm6/K9ImQZRnr7oX+Y0dROgdn3wzAueee01JmKSgoKASUf5VQOOusswgKDqamupql7z7GBQ/XZhxxu3DZqjGYT98AutbMlp8/QaVScaO3TbPOewC7XzS4XU4cdif6yDa0G3cn4V0G+9t5HDYQRQrX/EL2359Rnr0PfXg84d2HKSLhNMFQW+NAYwpHGxwWsHFLd63EemAbYV2H0v6y+gsQY3QSISm9sR7YfvLGx8BycCeW/dvQhsY32lc/rPclWHYtwJK+geiB4xs1RkPx2KzHvSYIAuYuIwjpOAR3VSnq4HBKN/5K8aovCet1AQkX3EPOvNewpC3H0KYXzuJ9VGydgy1r8+ExVK3HjUcQBKLPu5+ivw/HxTzxxBOMGDGC8847rwUtU1Bo3bREcLESzNx4/lVCQa/Xk5eby/PPP8/rr7/Ohm/ewe2wkbbwJ2TJy7UfL8QYqjwYBpI9i3+hIieDFJo3cAkgEi2l+NyHwjoPQhsSQYfL7z+qnVrvs63N2ZOJ6DkKr9NOcHyHZrVVofFUZadhydgKgCEisGLU66gGwJTYBW0DRKO9JKfRIqGm4AC7Pr4fZAlzr+PHRJwMbVgCgkpNRdrKZhMK7ipfgUtj/PHrWggqNdpQX12K6CFXEz3kav81Z1k2IGPP3UleTt33zxDfnbC+lwbe6EbiLM2sIxJqamowGpv/e05BQUGhKflXCQUAs9nMZZddxuuvv8723z6rcy1r4zK6nndVC1nWNDRVsSVZlilM20xFXibZW1YS3aEHvcffiEqjJT91E/tWzsOSd5Ci9O2oRBVnSxEBt+FkjCGSvyjGgoeKNJ9LQ8rFdyFqj++KYYhIaC7zFAKA5cB2ds160J9hR20MbDaLiF7nkPHTq2T//SmmpB5IbieG6CT0YUdHK3hdDqrz0jHGJOM5hSBkAGTJl/Go10WnNIzGHE911k68ThsqXdM/xGb98Q4Aps7DGtXfU1MO+Fb/ZEn2ZU8CzL0vIWrYTYExMgA4CveS+8uj6HQ6kpOT2b59uxK0rKBQX0QRmnuFv7mDp88g/nVCAWDYsGGsXr0aURQxmUyEhIQw6uxzWPXfF8lP3chZE+8gND65pc08JapK8pn3zC3YLeWY45KITOmGo6oCrdHEWVdNIyT25AWhjoUsy+TvXM/mHz+iKP3wil/OlpXsW/EH5ri25GxdhUqlRuuViUPHRVI06hYo2WFGwyR8D/6fko0b+YQiQeH0w2UtBVki5fLpmJN7o69HobOGoNbqiRk0nqL1v5M66wH/+X+mSpW9Xna8fwf24uw6/TWNcIMyxCTXDtook+sQ2nc8xYvfw1GSTVCbLqc+4Ek4FKMQ0a9xOxhRQyZRsOijOrEOKVO/RVS3nodwr6OK3F8eBaBHjx7YHU7cbrciFBQUFM5I/pVCQRAEhg4dWufc/ox9XH/9FL7++iuyNi7FHNuG7hdfT9t+I1FrtWiNphaytuFIXg9L3p5BdWkhwaiwZR8gPXuf/7o+2MyQG/9T7/FkSSJz/WIOblxKztZVuGxVaEQ13QimBybMqFlFBfsLc7EWZmMUNVzrjUVsJfX8vMi4kRFUSjrcMwXJ6+Hgnx9jTukNwMF5HzHk+T9P0qtxdLh8OhE9hpM5933sxVkA5Cz+gpiB49CZfYHvMjL24mw0QaHoIuKwFWURP/wqks67sZGzCtjzUwntddEp+dYa43sAUJ27u8mFgstSjKMk++QNT4BKG+T/O2bMfQR3GN5qfItlyUvR/JepPuiLmejUqRMLFiwgODhYEQkKCg1BbIH0qMqOQqP5VwqFYyEIAl9++QVvv/0WL7zwAqtWr2blx88BoNbp6Xzu5VQV52ErL6E0Mw0AU3QCgiBy8VMfExwZFzBbasqLMYZG1sn/Xx8kr4f05XP9doei5uraFfUSnESg4TMxn4ObltJvwu3oguonftIW/cTq/70E+Pz+Uwilt2SqIwRGEsFIIijDhVlStxqRAFCEE2h8ATaF1oXk9WDZv5WC1T9TsPpn3zm3A8njQlQ3zY5RWMf+hD3wGY6yAja/ei25S74id8lXAAx+7i+E2uKNMjK973z/lOYSRZGwLoOp2LMWe14qxsTGp3Gu2Ox7f0Lan3VKNp0Ie/FBijfOpWLXMgDiRk9rVME5j81C7p8+n/82V7yEPrZzIM08Zax7llB9cDORkZFMnjyZZ555hrCwwAXOKygoKLRGFKFwBIIgEBERwZtvvgnAihUr2LlzJy+99DL7l83BYbfXaV9VnAf43HECgdfjZue8L9n47bsAxHbtR78rpxKW2AFrYQ6bf/iQuO79ie/eH31IGOa4pDoPv/tWzGPlx8+hEkQ6y0aGcPhHLArfildHyUBaaSFzHr0WQRSI6dQHU0wbwtqkIIgi9soy9iz5BWeVhbjuA3BWW8jevAIEgavkWCI48YPYya43N5nYWEsFAEHxHVvYGoVTRfK42Px/1+KyliGoNSRdcCs5iz4nqu95TSYSjkQfEUebMTeQu+jz2jMC654aS7dbXiUorgM1BRkBmSdm4EVU7FmL5HE2egzJ5cC6dykh7c/CEJUUELuOpCY/nfIdiyne8BsAot5E/IXTCe85pmHjZO8k8/tH/cehfa9odSJB9nooWfYhZ59zLkuXLD55BwUFBYUzBEUonICRI0cycuRI7rrrLrxeXwWwQyXId+/eTc+ePel7xa2YouIDMl/60t/8IkGLSGHaFv58oW4O9vzUDRxKFthhxMWMuv1pitK3Yy3KY8vPswC4XI457gP7SCIwy2q2FRfgkb0cKM7H4/XUaSOKIqIkk77sN9SimnhZyxgi0J9mHxcnEn9TAkBk73PoePXjLWyRwqnidTlwWX2ZdTpNeoLIHiNIGDGhWW1IGnMDrsoirNm7cdTGJKR9NgNdeByiRl+vMYo2/UXGz69hTulDcGJX7MXZWDO3I8sSaoMJyeUAQB3U+JTN+X+9guz1kHDujQ3uay8+SHXOboLiO6IJiUKl1SNLXmyFB9BHJlKRupyc+R/620cOvIrYUQ2fR5KkwyJBVJFw6fMY4po+lqIhSB4n+b8+BsANU65vYWsUFE5/BJUKQdXM6VGbeb4zidPrya8FUdV+yA5tNbvdtUF77Y6fBrChHFjnK97TlWBGEkEhDg5gw4VEDnbaYECDiB0PJbjJWPkHGSv/ODyAINAe40lX9Xtjprds9h14fW5JmdiIQ48WgTBJixaRKjwESWKrciNqCHvwpbYM6zKYztc82cLWKAQClUaP1hyFy1KCLrT5Kg7/k45XPQz4djhyl39HzsLZOEpzSbrw1nr1z/jJV4nYsn8rlv2+1K4qvQmvowZBpUXUGIkYNBZdVEqj7JM8Lmy5O4gedDnGuIbvpOUu/C/W/ZsOnxBVIB1dLt3cZSQx59yCNrjhWc0kSSL9E1+BMnPPi4gaUb/3rrmp2rMER0kmP//8M1dccQXgsx1olIuVgoKCwumEIhQaSY8ePUhu147lHzxFTKdep1R/weNykr7sN/J3bQBAX/tgHoueWI69QulBYi0VeJCJQEscOiJkTaMe6qPQ+V2TjsR0mn88YmvvqTJ9Ix6HzV8vQeE0RhRR6XwFyGwF+zG1aVkXFVGtpe3oKehCItFFJFCyaT5pXz6Ns6IIld6IqU0n2p5/K6K67r+l4LZdqc5OI+mat3FV5KGPbo86OAJkGUF16v/uag5uBlkitPPgkzc+AkdZLrb8fT6RIIhoQ+MAGZUhBLelCE9NBYJKg7nrSKKHXOOvh9AY7PlpeKpKURnMrUokeKpLkbxuqjPWULV3Ke7KfFQqlV8kADz99NOsW7eOhQsXnmAkBQWFYyKqWiA9qrKj0FhO7yfBFkSj0XD1xIm88sor7Jz3FYOum97osdZ98TppC39EL6q5TIrGzMmz86gRGUHz1yY4nYhBRxeC2SNVU5O/z58hR+H0pGz3ajJ+ehWPzUr0WRcSM+DUagwEkpgBF7H/t3co3rIAAFFrRHLZsR7YRsHa3+l5xzv+In41BQeI6j2a6uw08ua9QNLVb6DSBwfUnood8xBENUEJ9RNSstdD0bpfyVv8P/85QVTR6daPA2rXkRQu/S8AXruF4uUfozFFEdrn0gZnOZJcdiy7/yasz6kXY7PlbCd/7rNHnb/vvun+v//66y++/fZbXnnllVOeT0FBQaG1owiFU+C5557jo48+Ysfczxl47X2NyqojedzsXfIrIai5RlKKfQUaI76HjrTPH0dQaej30OdoAlyUS6Fp8brs5C77jtwlX6LSB5F43o20Oee6ljbrKHwuRAIdbv8OUa1FlmWq0pdTuPBttr8zFUQVgiAgHxET5LVV4nVWBVQoWNKW4sjfTczQifWOmchf/hWFq75D1AWTdNnjVB3YRFBy34DZdCxCuozEXuhL22xN9QmssnVf0f6On0/6Xeq2FFK5/XdsuTtwV+b7xti9kITLnkdtbHwmoposXwSYNmkIeF24cjezZ88eOnc+LLjWrl1LiDkUtVr5+VRQUDjzUb7pTgGtVsujjz7KjBkzKM1MIyqlW4PHWPXfmUheD+1RHl6bgtDaj7jXaQNgx3t3ctbDX7WkSQoNpHjTfHKXfAlAhyseIrLXqBa26NiEdx1KXnEWNVlbMLUfjCAIhHQ+G1FjIH/+a6j0wRjb9MJTVYyoM2HqOBRDfHc0pqiA2uEs89V6iBl8+TGvy7JMyca51OTtweuoxl6chctShCY0jo63fIwoigS17RlQm45F1IDLieh7Mc7yPARBJGP2XQAcmDWZhMteQB/d/rh9s7+/H9njRDii7oK7Mp+sr+8m5ZYvGlV7wZa3E8uOeQj6UIydzsNVvAdX7maWLl2KIAh06NABURR57rnnePbZZ5V0ywoKjUVxPTqtUITCKdKmTRsAHNbKRvXP27keURDpfyi4WCGgdCSY9gQxh0JKcKGPVHZtTjckjxsEgWEvte60lFXZqQA4S/Zjan84NiA4ZRAdp30bkNiD+qCN8FWndllL6lSG9jpryPj2aWpy05BrA5MFtRa1MRRT+wEkjL2/2YNzRbUWQ3Q7ANpNfpXcuf+Hu6qE4iXv0HbS28fsYy9I84sE86gH/eerN3+JpzyT0lWfEjXytmP2dVsKEXVBqPSHa8jY8nZh3b2I6n0rANB3OAcAdUgCKq2RO+64A4Bzzx3N4sWLAKUmi4KCwr8HRSicIp06dQJgw9dv4bbXENftLAzm+qU0LNq7jerSAiJpXBCyQv0pwQVA95sVv+LTDcnrBllGkqRWmWVGkiTSv30ea+YOBJWWsL6XHdWmuUQC4HfFqdyzhso9axC1eorX/oLHbvXZotYSNWgCUUMnt6r3MyihK51v/4xdr43HVZ6Do2gfbmshuohkbHm7cFvyMSb2oXiJr6Cd7KpBljwIou+9DT7reioXPocldQHhgybjKs9GF5mCqNEhue1kfXMP3ppyALThiSROfANPdRn5vz3lt0FQadFE+HYyRL2J4OHTceZvw7HnT5YsWUxOTg6JiYnN/M4oKJxZCKLY4IKygZhToXEoQuEUGTBgAE8++STPP/88i9/ypUyM6dSLAZPvI65rv2P2cTt8hduyt6wEqFMYTSHwiAgEo6IaL7aSHIxRyg/96cShzEbrn74Yc4ez6HL9c63qAbcyfQNlO5cDYIjvhqA6eTKCpsTcdQyV2/+gcNX3wOFikKIuCFOHQSRe9EDLGVcPYkbeQNHy2eT+/MhR1yw7/qh7vHgmpqF3IRrDEQQBTXRX3MVpZP7PV+/A2LYfEYMmk/f7M0jOagRdCLKrGld5DhVbf0Vl8O3kGnpcjiaqM4jqOp8tQaVGn9gflSGUmq3f0LZtW+655x5eeuklgoKCUFBQUDjTEeRAlRX+l5Ofn8/TTz/NvHl/UFhYAMCIqU/idtg4uHEpxtBInNUWqksKsBRm+/tpVGqmeONQKzsKTcpBbCyghLYX3ELiOde2tDkKtcheL16nDbXRdMJ2xZv/pmjTX1gzt9Pm3OtJOv+mZrLw5EiSxOaXJ+GurkCWvKiMoaTc8EmLCga3tZjML6aBWkf7615DbTCjCW588bbmpiY3lZqs7SAIeO1WDPFd0UW0Yf/n9wLQ8e6fyPjwGmSv299H3+k8NLE9qVrxxjHHFNQ6gvpdh+x1U7P5CwB00R1xFu8jZMR0RP2J48S81cVUrf3If3zxxeOYPv0+xoxpWCVqBYV/K4cK1lZsWUSIqXmFtrWqhrB+Y7BYLISEKDGhDUERCk3A9u3buWbyZNJ27/afU6nUiF4JHQISMgZUxKKnLyEEKRs7Tc4hoaA1RzHg0e9b2hwFwGkpYfdnM7AVZhLWZTBB8R2J6nMuxuikY7Z3WcvYOHMCgkrD0BcX1LlWvHUR1Tl7SL5oKqL6xAUHmwpJktj4whV4bFYM8d1IvOLFFrHjEAUL36Jq73LCeo8l4fy7WtSWQCDLEhmf3oWzPAd9TCdCuo1G9rqw56VSvX8dAIIuBE1kB1x5Wwk55xE8ZRl4yvajTxlVRwhYlv4fsseBoNKgCm1LcL/6Lx54KnPwlB3AXbANr91CSkp7vvvuWwYMGBDwe1ZQOJNQhMLpifKE2gT07t2bjRs2MHbsWFauXEl7jIzxBjaziULDiKstXCdqji4sp3CYoo1/4rKWETPgYrQhTbsCnbvsG2yFB9FHtsF6YBsVe9aRu+RLtCERxA29nMhe56APjwPAmrWLnR/6VpOPrMhsL8tjxwd346mxAFCdv48et75+VIGz5iDjx1fw2HxxAF67pdnn/yeaYF8RSGdZTgtbEhgEQaTdNS+T//f7WPetwVGU7qtGf9uXRAyYQNZ3DyI7regS+2PsNg4AbUw3tDFHZ6MzDbkdr60cTXhyg+1QhyaiDk1ElzICZ/YGsrLXMnrMeVRWlLcqlzgFBQWFQKAIhSagtLSUxx9/nJUrV5IkGBkjKyKhJXHg5XNyAYjoPqKFrWmdOCoK2f3pDOwlPre4nMVfEtVnNG1GX4fHUUNQTErAH77Ld68GQeCsh3xuIBX7NrHvh1dwWcvImv9fsub/l6C4DmjNkVTs8a0Yh3cbTtcpzyF5XNQUHGDH+3cCYEgehKcyj6qDO1n/7CUMevaPZn9o04Ycrs4ee979zTr3sQhK7o9l90JsubvI+PweOtzwbkubdMqojWbaXPIfyrf+SeHSWSDLiFoj+uj2GBN7Y8vZTtW6TzCPfhwkD4L62AsDoj7kpK5GJ0MQRPRJg1EFRVK19RtSU1Pp2bPp08oqKJz2CAIIzSyqlUxljUYRCgFm7dq1DB06FAA9KnrLJ/a9Vmh6/qQYAFGjp+3oG1rYmtZJ4fp52Euy0cX1IKTneMpWvEvxlgX+SsOaoFAGPvlLQOdMGDGRzHkfcOD390gZfzdhHfsz8PEfkSSJoo3zOPDrW9QUZFBTkAFAaOdBdJ3yHB5HNZtfuRaPvQoAVXA04UNvw+uoonLDFzhyt5K94H8kjz12isymwnpwBwARg689YQ2A5sIQ14W2V79O5uzb/Dsupwuy10PO3JepytwKskTs2TcT3nssgkqNqNIQ2f9SarK3U7V/A5KtEjE4nISLH2HfR5MBsCyudftSaRE1RgydL0ATXb8q1Q1FZYoBYMKECezZs6dJ5lBQUFBoKZR90gCjUh0u6nEDbfwuLwoth4hvJaHH1DcQtS3jv95akSQJS+ZOCtfOAcDU/WK0kSnEXv4GMeNfJnTQjWjC2uKuqcRRVhDQueOGXUlIUg8K1/2Go7LIf14UReIGjWfAE78QFN+RAU/8wlkzvie8yxBWzziX9c+Mx2OvQp94FmHDphFxts8lSaU3oWuih8GTIUkSNfkZaCOSiOh/VYvYcCxEle/z7nVUIUmek7RuPVTuXoJ13zpkyYPsdVOw+GPy5tetqxAz0if6c+Y8DYCoNdLx7l8wtu2HIb67L5jc60JyVFKz/fs6gc+BRNSZ0CUNJisrm5KSkiaZQ0HhjEIQW+al0CiUdy7ADBw4kNtu861kelDixFsDZxMBwI737yTj59da2JrWxbonLmDXx/fhddrQxXRFF90R8BWUUgdHEtR+OLrYrgBIHldA5xYEgQ5X/QeALf93Henfz6xzXRscSp97P0YbHIo+NApd2OHYhOBuFxE+9FaMSQPQhMT6z6tr/67M2IwkSVTl7EHyNP0Dcum2xUhuJ5qQ6JM3bkYErQEA2evGVZbXwtbUn6rMLQDEjJpK0qTXQVRTuXspLmux/3rGZ74AbW9tfQjwiczEy56i7VUv0umuH2l/6xfoonwF3ao3zW4ye3XJw3F5PLz55ptNNoeCgoJCS6AIhSbgrLPOAsCrCIVWQSgariAWAV+w7sG/ZrW0Sa0CSZL8FXpjL3uNiHOP7Vevb+OrB7L1rVvqrPwHAkNUIt1ufgVBraFk6yKKtyz02XYMURLeZTDG2JRa493HTD+qNscDUJOXzronLmDH+3ey7umLqcrefVTbQBKS0gfwVf5tTQiiGnM3X/rOjC/uxV50oIUtqh+HMlc5SzPRR7cnaviNAOTOe438xR+T9ZOvSJomJJa4C45fF0JtDCH5Gt/Du9dagD19ITU7fqZ681d4yg8Gzl6tEXVML1566SUyMjICNq6CgoJCS6MIhQDj9Xp5+aWXAPAgtbA1CoeIQsdYfKu9+Su+a2FrWgeiKKKPbIM6JBaVMRThOFuzuqj2mPtNAlli+zu3I0mB/VyHduhHl2t97iP7fnyFNU9cyNonLmTd077MNUfOlzL+HgCq9yw85ljqoHAixzyCJjwJQWsEfKvpOz64m3VPj2PtUxex9omxbH3rFjwuR6NtliQJj8vhF07aYDOCSoOrPAdnaVajxw00giAQc+5dtL36DZC8ZP/2Ih6HraXNOinGBF+mouCOvuQD4X3GoY/tjC1vN+Vb5qIJSyDxyhdJufEjgtr2Pul4Kr0vVsyZtRZ3USqe8gNUbwvs94Chk0+QvfDCCwEdV0HhTEMWxBZ5KTQOJZg5wEiSxMEs34OCUh+hdZGIgbYYyJbtOCoK0YfFnrzTGY7kcuCxllKzbxlBHc8+brvgLmNwFO7Gmb8DV0UR+oi4gNphiEoiJKkHXo+Tmrx9AHidNnZ8dC9VB3cR3X8sHa/6D+aU3qj0wXgd1Vh3/EZIr0uPGksX3ZHoC58EwHZwA7bM1YjaIGS3DZUxAtnrxpa5hr1fPkX3W/6vUfZue+sW7MXHFgSW3QuJHnlro8ZtKvRR7QjrM56Kbb+T/tEUutz9TYvVmzgRsixTsuZbbPm+oOCqvcsIatMdgKSJr2DZs4ygNr1QN7B4XPKUD5FcNYjaIKoPbKBo0TsYOp0XWONr40E+//xzZs+eHdixFRQUFFoIRWIFGI1Gw3PPPQdAJq1/5e7fRheCAdj6+o2Upa5qYWtans61K/mVG7+iJmPFCfP/m7pfDMC2d6cG3A59eCw973iHPvd8zLCXl9D3gc8AqDq4C4DiTX/52/ac5nMlqdo196TjGpMHEnnO/YQPm0rE2dMJHXg9YUNuRtQacVY0zo1q30+vYi/OQheZDICoDcLc4wL/9UOr162NqOE3ET3yNiS3A+ve1S1tzjEpXPpfitd8Q/XBLaiCwjB1HlnnurnL2Q0WCQBqfTDakBjU+mCKFr8HgCa6S0BsPoQgCKjNbQB4993TPxWtgkKToQQzn1Yo71wT8PjjjzNo4ED+poRtWJCVWIVWQzuMDCcc2eNiz5dPUbD+95Y2qUUJSepOx4mPAlC54QtK5h/fbUIX1R5D8mC8jhpc1vImtcsQ1RbEujtyruoKUj99hG1vH057KsuNc4MyJA/GXprDzo+nU1NQf7/93bMfp3jTX2hC40ia9Cad7v6VDlO/Iubs20ma/A7RZ99BeCvKevRPVMYwALwuewtbcmzKt/sEYcc7f6DDLZ8R1CawdQksaUtBllBHdkDQGAM6NkBQv2sRtEYeffTRgLvoKSgoKLQEilBoAkRR5M+//uLyyy9nPZVsw3ryTgrNRndM3EQiEWg58Otb7PzkfiwHtre0WS1GdL/zUOmCADAdw5XnSAyJvsDm7MWfB9QGR1kBG2dOZMeH91CVvRt3VQWieDjVsC4sjvTvZ1KZvhGAkF6XETfx/ePGVZwMU8/xAFgzd7Dt7fq7CdmKDwKgNoQieZx1runCEwntcT7CEXY3JS5LAUXLP6ZkzZfY8nbhshydvtZdVUJ15gb/cVX6cgAkb2AzWAUKtTEUBKHJ3KLK1n8LgKHTBQhNUIBJUOswdB1HTU0N33//fcDHV1A4IxCElnkpNApBlmVlubuJcLlcJCQkUFpaygVEkUzgV7AUGk8VHv6mhFJ8D01hXYeSfNE0jFGJgC/epGLPWsI6DWiV/tyBYucnD2A9sI3QAdedME4BwGuvpPDXh9CFxdH/ka8DZsOGFyfgrio74owAAlD79SSIKn+GJmPKUMIG33zKc1p3zqVq52+Y2vWi17S36tXHcnAnuz6+H2p3Mjrd/esp29EYnKVZZH03/ajzoi4Ild6EMbEPgqiicuefIMtEDJyEdc9S3NbD7lbdHvy92atX/xOvy07Nwa0EpwwA2Uvae5NR6YJpf8unTTJf+ntXIUseVMExCCo1Qf1vQgjweyDLMpZFzwPgdrtRB7iiuYLC6YrVasVsNlO+cxUhpuDmnbuqmvCew7FYLISEnFpV9n8byjdYE6LVatmzZw+RkZHkYFeEQivDhJoricOFxBrK2Zu2hoq0NUe1EwQBWZbRmcLp8+AXqPVn1n9H64FtqEPiMHYYddK2KkMoph6XULVrLqtnjCYkpTfJF03D1OboQmdFm/8mrGN/tCEn9yk/JBLMZ03Csvl71KYoPFXFoNKA140seTEkDUTfpi+6mMD4lhsS+1K18zdCEus/njm5J4Oencf6py4KiA2NxVq7M3AIfWxntKEJ2Av34rVbsKT+jSCqEbVBSM5qyjb4MvyI+hAkh2+HM+2tK+n+QMsInUMUrZhN+dY/8AlDAWSJkL6jm2y+kK7nYEldiLfaJ5hktw1BF9gHFkEQMHQZi33PX2zevJlBgwYFdHwFBQWF5kQRCk1MREQEQwYNZvf6zbiR0CjeXq0OLSJnE0lvzOylmmKc6FFRgpNwtCBDPg6cVeVsfO5SQrsOpd1Ftwc8809LkL/qZ8C3U1BfVwxTz/GoQ+Ko2bcUa+Z2drx/Jz1uewNzyuE0lUUb/yTj59dQ6YPo//C3qI0nfhgTVBpkrxtdXA/iJ50LspeajBXoE3rhyN+FLqoDmrDExt/oMfDaKgHIW/kjyRfdXu9+nqoKAEy1qTtbgsjB1xKc3B9L2hJsuTtwFKbT9qqXj9nWVZFPwcI3MXe/gNDuY5AlL/s+uArZ6ybnj9dIvPihZrb+MD6RAIJKjagLIqTL2UQMuqbJ5osdfRehfS7BUZBG0ZIPcRelomt7+EFedjvwWHJBVKEObdtoNzJ1RHsAbrrpJnbvbtoaHgoKpx2i6Hs195wKjUIRCs3A/Q8+wMSJE9lNFb0xt7Q5CschDA2DCTvu9VJcrJMqyEtdSXnqSpIuvI3qvL0EJ3SmzdlN93DTVNQUHSRz3vsAmLpecJLWhxEEAWPyQIzJA/HUlFP028Ps+uR+ghO7kDx2GqJW76+A7XXUsOGFyxn4zFzUWv3xx1SpkL1ukKVaVxCR4M6+leXgTuc0/iZPQM2evwEISuh0wnYZv75F0fq5CCoVKn0wHptvRV7UGanJ2kJQUr8mse9ECKIKQ3w3DPHdKFr6IZbUv9n/6U0Ete1L9Nm313GV04bFkzTx1Tp921z2PLlznqQqY8Oxhm82zF1GYtmzAtnrRh0cQeTAiU3uDqWPaItcW9DPkbnKLxRkrwfrqneQPb76GqqwJEz9b2jUHCpjOILORFpaGjabDaPxzNqFVFBQ+PegSKxmYMwYXyEeu1KA7bQmEi3jiGEMkQBkzZ9F2c4VZM2fxd5vnyd32bc4Kkso272amoL9AHgctlab/URj8PlpmrqPw9RjXKPGUAeFE9J3AoLGQHXuXnZ9cj+p//WtUBuSh2BsPxxZ8rL/51eRPC5sRceuPRA3eDwgUJ22oFF2NAbJ5UtfHBTbDrft+AkHynYtB2RkrwfJ662NTxCw7FpA3tzncVmLm8fg42BM7I06KBwQsO5ZSsZHk3BVHh3YfCT62I6A7z1Ie+/aY1bCbg4SLrqfuNHT0IbG4SzeT9aPjzbLvCqDb8FGdtUgux3IsozjwHJkjwNBrQPAW5FF9Yb/NXoOQ2ef+F69unWmolVQaCmUgmunF8qOQoCRZZnXXnsNjUbD9OnTAV9tBYBstYvBnhY0TiEgtCcIMxpysaNGZDXllG5fSun2pWTNn+VvJwgisiwR2rE/kb3OJqzz4Hr56wN4bNWUbF9E3JDLmuguQBsSjqjRYzu4DlPPSxrtZmHqegGmrhcguWxU71nor29g6j4WdUgstsx1/vcHIGbAxYS074Ot4ABtRl2DJLlxWkpAEJD/kUmoKQkdeiuWTV9TvGk+xZvmE9VnDDEDLsIQk+yLQxHVlO5Yitd5OJWo5KgCfGlGvQ4rSB5yfnyYoOQBhPYZh3X3Imw523GV5wC+LDjq4EiSJr/TZCvlpg5DMXUYCkDljj8pXjGLsg3fEXf+/cftI6p1dJj2HRkfT8Jrt5D57SO0v/7NJrHvRIgqDRH9LiGk0zD2fjil2TJGaUOiauNgSrAsq1t0r8PUL0GSOPD5NDyWPCRnNWIj4hik6hIAwsMbXvdBQUFBobWgZD0KEA6Hg1mzZnHvvff6z1VWVmI2+1auLr/8cubMmcNU2iKgpOk6U/Ais5dqnEhEoKUYJx5kZGTKcJOHo077jpMeJ7LHCEp3rcRVWUTswPF1/Pdrig6y88N78TqqAVDpjPR/5LuT+vg3lh0f3ENVdiqIaiJG3Ik+odcpj+muyMFRmIap6/l4beUUznm43n0jzrkffVz3U7ahvsiyhD1rI1U7f8dTdUQBNkGszbgkgyAQ3G0sssuG5KzGkDwYfVwPAKw7fsV2YA2Ss+pwV5UGWZYQdSZktx3Z4yRi0DVEDJgYcPsrd/yJozgDXWQ7ZK8br7Oaii2/ojZFkXLDJyft77IUcPDLO/3HkQOvInbUjQG382R47Fb2vDcZgLCzriRywJWIWp+7jr0wHVv+biL6XRbwefP+eAl74T68NYfrgqiCwmkz/ilKVn2GLWc7Qf2uRVMbc9AQanb8iLsojdtvv50PP/wwkGYrKJyWHMp6VLZ7XYtkPYroNljJetQIFKEQIO6++27ef/99zGgIQiQfJ7t376Zr164sW7aMSy65BE+NnSlygiIU/kVU42E/NeTjJJtjF7mK6DmS8K7DyFrwX1yWkqOui2otfR+cjT4sNqC2HfzzE/JWfHfUeX1Cb4wdRmEIgGgAcOTvoiZjOSE9xyNLHkoWvOi/Zup1Ge7yLHQxndFFdw54wHJ9kb1unEV7cJVk4CjYhTYiBWdhKprI9oT0HI86OOr4fWUJV/E+bJmrCep4DtqIdv5rnupSin6fgTYiGZU+GHtBGlHDbiCs9yWnbLPbWkzmF9OOeS20z3iih99Ur3GcpZlk/zgDWfL4YkRUGnSRSSRccC+GmJRTtrM+yJKX1Nfr1vBoO+Fl8ubNxGv3uYV1vPOHJklT7CzL4eDX9xz3evCg21CHNDxxgeS2Y13miw1JSkriww8/ZOzYsY22U0HhdMcvFNI2tIxQ6DpQEQqNQBEKAeLjjz/m9ttvZxzRhKDhW/K4ZvJkJk2axPjxvuJOY4mmLYYWtlShpcjHwWrKKcdNF4KJQ8dSyuq0USPQAxP9MKNBZAHFHKwVGKJai6jV0/6y+4nsdfJUpidj34//R/Hm+YAv/sKDhAxYqPWPEzVEnTcDbUTSKc91JK6KbGwH1hLUYSQa8+mfOepEyF43JX/PxF2RU+e8LqYjkQMnnVIgdPaPj+AoSifh0mcwxHTGXV1C1b5VhJ91ZaMeqGVZwrJ7EaVrvkRy+na0EATaXfN/2HJTKdv8O6YOgwjpOIS8Be/gqSpF1BoRVGpiRt5AeK8L8Ngs7PngetTB4XS69ZMG21GTm0rx6q+pyd5x1LXO985p8D3Vl73vXQmSF0P38eD1YE9fCJIbffuz0aeMbPS4steNK3879j1/AnDu6NH8/NNPhIaGBshyBYXTB0UonJ4oQiFA1NTUEBwcTEeCOJdItmFhPZX+61cRRwRnbtEuhfrjQUJdm0egGg8VuKnATRAq2hN0VPsdWNlFFVUcDnAZMnPRKfu85674nqw/PyYSLVdy+IHdjcQSSjmIHVEQibn6A0RRCWdqLLIkYdnyHXjdCLpgqnf/5b8Wd8GDmDoOB8BTXY7XVYPXUY3aYEYbFn/cMSW3g4yPr0EX2Y6kSW8E1l5ZpnT1bCq2/d6gfhpTJO6qUv9xcLv+JF/1TKNsyPj8PhzF+wnvczGemgqqD26m4x1NV+m4Jncnub88CUDoeU8he5x4q4tRhwZmh8tbVYgzbxuunA2899573HXXXQEZV0HhdMIvFPZsahmh0KW/IhQagfLrHyCsVt/2+CGnoj6YiUVHIU7aYcSMpuWMU2hVqI9INhaMmmDUJJ5gp6kXIfTAxCyy/edKtvxNVJ8xiKdQ9TVn/n8B327CkWgQuYBodmBlrVxBdepfhPQ8dVeZfyuCKBLaf7L/OKT3Fbgrsilb9jaFi97Bsmcp9vzdyO668SwJlzxNUFKf4wwqIKg0OMuy8NisqI2B++ETBIGo4TcRMWgyjuIMajI3ootujz66PTVZW3FbCgjpei6CIKI2RSF73WR9O72OSEAQcR1RBbqhdLjhbf/fhcs/Q9rXdEHutrxUv0jQxPYEaoPQAyQSAFSmWPTJw3DlbODtt99WhIKCgsJpg5IvKkAsW7YMgKraUFaAWPT0wayIBIVTRkRgAnH0xvdAmPHT/7Fr1vGz2hwPyePB46jGcnAnkuQlBSMjOXZWls4EEYyaqp2/Ibkcx2yj0HAEQUAbnkTEqHtB1GDL2oLsdvhy76sP15rI++NFStZ8gddRddQYolpH+FlXgixh3bv8qOuBQNToMCZ0J2r4jYR0GoE2NJ6w3hcTPfJW9FEp6CKTUemCUBtDSbn5M9pN+ZiOd/1CwiVPgCyhDUsIiB36aF+cRE1uakDGOxJHWTY5Pz/uOxA1GLtdHPA5DuGp9An9ffv2MWjQIPLy8ppsLgUFBYVAoewoBIhLL72U888/n7///ps/KGYs0aiUoGWFABKOlsFoMaNmBeXU5KbXu+/uz5+gJi8dl7W0znkP8nGD63Wo6EMIqyineN7jxF7x+inZr1AXbUQycVe+ie3AarRRHVGHxFL46wO+gl9qA6JGS8WWX6nau5zYMfehj+tSx+c/KLk/ZRu+w20tbMG78CEIApqQaAD/zoIY4FSnbmsRENiMWLk/PwGAoctF6BL7B3Tsf6KN7Y6nbD+u/G1s2LCBNm3a8P333zNxYuCzYSkotGoE0fdq7jkVGoXyzgUIo9HIggULuPWWW8jDwVyxBC9K+IdC4Amq1ffHczep2LuR7R/cRer/HsZR4XuIrEhb4xcJOkTaYaQjQYyuLR53PLpjoiNBeB0WZVehCRBElT+o25m/01+nIWTgDYSf9zhBva7E66wm97enyZx9K7bcHXX6Ar56Dq2I4BRfpWNr+pqAFHKTXL5gfmNc51Me6594nVUIenOTiwTZ48JdmoG+4xjMo5/A2N2X4enqq69WdhYUFBRaNYpQCDCPPe7bxrbibmFLFM5UotAiABpzJB5bNZLHg60kh92fP0HpjuWkzX6U6uw0KvdtIv37lwBfPQaAEYQzhTacTxTnEom2Hl8Bys5Y86CJSPavernLMgAwJA8i7PwnCOp9FZLXS+6cpynb+COyLCHVxjS0NqGgNoZiiO8GyOx5bzK5f73J7rcnULz6mwaPJXlcOMt8GaPUQWEBthREbRCyw4Lj4NqAj30IZ+FuLMv+j5qt32Bd/jrusgNo43tjGn4PKo2Ot956q8nmVlBojciC0AKVmZXfscaiZD0KMNu3b6dPnz50IohzTrJaq6DQWNZSwQ58D4giQm1iUxBFNZLkIQItoag5gA1NSCQuaxkgM4E4whuYfWsv1SyjDEGlI/7q9wN9KwpH4CrdT8nfPnEX1GM8hpTh/muSx0X5/GdA8hDS9Vxkr4eq9BWojKGk3DALQdV6PEnd1mIOfnvfUQHah1DpTXS959vj9ndZi8n/+32qMzf7Tggine/5JeB2Si4H+z6aBIA6PBljrwmImsClsJYcFqwr3z7qvKANRt/5Ajyl6bgLdlJYWEhMTEzA5lVQaI0cynpUmr6VEJOpeeeuqiKyU18l61EjUHYUAkzv3r25//77SafmuAW2FBROlYGE0gMTnQkiqTZjUkeC0EgSAGW4GEQY7QmqdTnyCYnyRux0dSaYaLTIXifW1D8Ddg8KR6ONbI8xZRgAjpzNda6Jai2h5/4HBBFr2hKq0leCIOK1VVK5a35LmHtcNCHRtL/lcyIGTiL2vPtJuelTQrqN8Vdb/meAtsdho+rgFgDsRQfY999p1GRtRxUUjqg1EndBwwP364Oo1RMx6BqfDeUHsS57FUfm6oCNL2gMoNYddV52VWPf+TPugp0AHDhwIGBzKii0eg7FKDT3S6FRKDsKTcCqVasYMWIEbTEwluiWNkfhDMeDTDY2kjBSg4dvyUeLwI0kItTuNmTVitZkDI2qDO5E4icKqMFL2Mi7MbTpHejb+NfjdVixbp+Dbf8KAEJH3YfafHTmIMnjQvbYUenNSJKH8nmPARA14mZMHYY3iYtOIEl/73L/30lXPQcCZP34FAAhnYbjrirBXpBO8rXvoItonkrdNbmp5P32LLLXF1MRMupBRO3RNU0ag+SyUb35C6TqYgAElRZZ8gIyyD5hn5aWRpcuXQIyn4JCa8W/o7Bve8vsKHTsrewoNAJFYjUBh3xO+6B8GBWaHjUCKQShQiAEDROJ5xoS/IJARKAdRtphbJRIAF8A9GXEYEJFxYp3se6cF8hb+NcjyzJFcx+rFQkC4Re9cEyRAL6dBZXefOgIlSkGQaWlZOWnHPjsZlyWgmazuzFEnz3N/3fWT0/5RQKANX0V9oK9BLcf2GwiASCoTXc63fUDUSNuAUAKYNyHVFOGVH0425jsdYHsBVlCDPEV1fv0008DNp+CgoJCIFGEQhPQoUMHACXrkUKLEIYGPYFNTXmI9hgRgKqdc2rTVSoEAndZJrLbgaAxEH7R83XSoJ4IURQJO+dBIi5+AU2k73vn4Nf34rYWN6W5p0RojwvpdPevxI19BENcV3TRHYg5926MCT38beRaF7rmpHzbPErXfAGiGtEQhizLyLLEqW66V2/6DJDQJvQ7qoibXFVAUnIyAwYM8BftVFA44xGElnkpNIrWE/12BnH++efzyiuv8AfFXEUcEQ0MHlVQaI2so5IMagAQRQ2qALlm/NtxV+ZR8vdMAIJ7XVFvkfBPzEOn4ireh3XdLOz5u/11DVorpvaDMbUf7D8OThlIycpPse5dRk3mRqzpKzG27YtaH9ws9pSs/BRkCXVECq78bbgLduCt8qUX1rc/G33KyAaP6ak5vJNg7DYOWZZx5W/DdWAZXkcVsiyTdfAgEydO5NJLL2XOnDmBuh0FBQWFgKAIhSbg3HPP5bvvvmPSpEksFMu4UopBo2zeKJzmlOHz3xZUGkKHTkVspge4M53qvYsBUJvboEs4tdgPdWQ7QMBZnhMAy5oXld5E7Hn3Yeo8irzfn6Vg/uuAgD6mA+EDJmBKGdik84f1HkfFtt/xlB3AU1Y3uNixfxme6hK81nxkpxUEEW3CWRg7n3/c8VzFe7Dv9rno6ZJ9AeqCIKBL6Is2phs1G/+H4LTi9ngRBbjxxhub7N4UFFoVSsG10wpFKDQRV199Nfv27ePJJ5+kDBdR6MjFzj5qGEKYv2iWgsLpQn/MLKQU0RCKIbFvS5tzxiC7bQAE95lwymPZ9y0FZJA8pzxWSxHUtg9Rw2/CsmsBLmsxjqJ95M+bSfSo2wjrfXGTzRs98mbCB01CFABRDaKIKKrJX/AWVenL8RSl+hqKvu9uV84G9O2GImqPFsySx4Vt+w/+Y11iXZEjqHUYek6gau2HaDRarrlmEpdddllT3ZqCgoJCo1EkVhNyww03YA4x8xtF/Jds5lPCfmx8RR6/UcgBbC1tooJCvUkhiB6Y8FaXYNn6c0ubc0bgqSnDnr0JEBCDIk55PNlZDYCx7ekt5ML6jCf5uvdJmfIxsedNB6B4+SwqUxc16bxqnRFRa0RUaxFrBUH8BdPpfM+vaCPbARB9yctEnPMgyDLWle/gyFyNI3M1tn2+nSFn7masy17xj6lJ6IuoPzrDiyo4Cl3KSNxuF88995z/vMvlwmazkZeXx6233orDoVREV1BQaDkUodCEJCYmsjttNw888ACiKBIfF8cbb7yBXqejECcLKWEV5Thp/sA9BYXG0Ks2k5ezeE8LW3J6IssyjrwdVO2aR8X62dTUuh3p2vRtdGyCuyKHimVvUfrH4ziy1vvmqU3zebqjDg6vc+y2FLaIHZIk4SrLwpA8BEGlRh0SS/g5D6AyhOLIWIwjYzGug6upWvcJ9rQ/QJbRJ/l2Edx5W3GX7vOPJcsyHmsBrqI0REMoALfddptfEFx//fU88cQT5OTk8L///Y9ly5Y19+0qKDQpzV+V2fdSaByK/0sTEx8fz+uvv87rr7/uP3fttdciSRL/eeghvvr6awpwMIH4FrRSQaF+GFERh56CskzK180mfPCNLW3SaUV16h9Yd8w56ryh/YhGjefM30nV5m98VZklD8gyKn0IxsQ+p2ZoK8FtLaJw4VuAQLsbP0Ib0jLVi0tXfw6yhCo4yn9OE5ZIxHkz8FQV4yreS/XO33zBz4KIqddlGNuPxJG1AYCard+iSx6OqA/Bk70Wt63CP05YeAQrVqzgl19+YfLkyXg8HjZv3kyPHj3o3r07ZrP5KHsUFBQUmgtFKLQA0dG+bCRffvUV1TU1zJkzhwrchKFpYcsUFE6MCoGxRPEpOTgPrgVFKNQbWfJi3eULbtWnjED2uvFUZBHc+8rj1kw4hNdhwb5vKdq4nqhDEkCtpXrLN7jyd6AOjqTtxFdR6U1U7vgDQ0KPRu9OtDaKV8wCILTnhS0mEvIXvEnV3uUAGNoNqXNNENVozPFozPGIumBs6UsIHX4HKr1v5y387Adw5G7GlrEc58FVgG/xqEJ2kJmZSffu3Zk6dSrx8fHExcUBUFRUxOrVq+ncuTMDBgzgwrFj+erLL7nkkkua8a4VFJoQQQRRCWY+XVCEQgvz6quvMmfOHBZQQjIGglHTlWBUjSyMpaDQ1GgQ0SDgVdKjNghBVKEyhuOtLsbYaTSi1njC9pY1s0ClRp/Yn6rNX4Ms4chcc1S7hEueQG0MBXy+/WcSKp3Pt/+fLkjNxb6Pr0Vy+lICmwdMQdQYjtvW0HYAhrYD6pwTNDo81SW1BwLIMtHR0Zx//vlER0czdepUoqOjee31NzirX1/OOeccpkyZwurVq8nJzeOpJ59AEAQGDRrUZPeooKCgcCIUodDCdOjQgblz5zLhqqvY5anB6/WyUbTQSTKSiIFE9CespuvAiw6x0RV3FRQagxEVVcpnrkG4Sg/grS5GDIo8oUiwbvwCV9FekNwAuIvS0IYn4vpHylNTx+FEj7wNleHMrQAfMeRarHuXUbbhByIGnHpWqIZySCSEDrkVXVyPk7Sui6e6BMuKt4mJiuCumTO56aabKCkpITQ0lDZt2iAIAjNnzsThcPDd9z8waNAg7r33XkpLy7BarahUKoxGI0OGDDn5ZAoKpxNKetTTCkUotALGjRuHpfaHYfXq1XzyySd8/fXX7KKKGFHPhVJknUq7LiR+E4spl5wAhIpaOktGYtBhRoMOUdmRUGhS4tBT5axqaTOaHcltp/iPp/HayjH1HI8xZRjqemYrKl/1IYgqzMOmHX98jwtXwS4AdFEpaMxxiFoDEQOuRtTocZYeBFmiKmM1UcNvRtToAnFbrRZNcCT6mE44itKpydlBUGKvJp/TVZGH21pEUFI/wvpcSsW237BnbWiwUKjZswCP08aK5TuZM2cO//nPf5g2bRo9e/as006tVrN2zWqysrJo186XWSl93z5Wr1oZsHtSUFBQaCyKUGglaLU+n+KRI0cycuRIZs6cyZYtW5hy3fX8ai+mnaSnDyHoUVGM0y8StFotCR06sHnvXjzeSgA6CMGMlk891aKCwvEIQ4MkS0geD6L63/E14rVXUrr4dby2cgCqdv5O1c7fMfefTFCHUVTt/B2vw4IupivG5LquIvbcbXhtFWjjeqLSnyA41XM4W1Hs6HvQRSbXuWxs43tYNTbDA3NrQWOOxVGUjqMoo8mFgrMsh4PfTgfJiyCqkWvrUXisDc+2pDKEAdC+fXv/ua+++oq///6b8847D4D09HTOv3AsVkslFeXlhIeHI8syPXt0R/0v+XeloKDQulH2Ylopbdu25bLLLmPx0iXEdkhmO1b+EkuRkSmtrZA7e/ZsnE4nu1JTqais5MUXXwQgQ67GgbclzVc4w1HX7lh5bWUtbEnzIEteiue/iMdagKDWETn+/wgdMwNBY8Sy6Rvyv5tGVeof2PavomLNLLyOw7ststdN1Y7fEDQGgs+69oTziPpgQobeDkDRso+a9J5OF8L7XYaoN1G65gtqcnc26Vz2on0geRH1ZtThSahDEzD1upzwcx9s8FhB3cYS3M1XIE4VEkvkhU+hi+nC+PGXsm3bNgB0Oh0et5uK8nJAoLy8goqKChYuWkxYRCQpKSkUFxcH8A4VFFoBh1yPmvul0CiUd66VM2DAAPbs2cMrr7xCseTgT4pZTyVRkZFceeWV/nbBwcE89thjfPPNNwAs49/xAKfQMgTXbkZWp81vYUuaB2fRHiS7L6VlyFCf65DaGE7I4FtAVCGodeg7nE1wv2sAsO1fgSxLOPJ2ULb8PdyWXIzdLkasR6YPbWQKiOqTBjvXB8njourAerJ/msG+Dyey/783YN2zHK/r9Cn2qItsR9sJvgJmFdvm1rlm3buCyt1LkKTA1KIpXf05AIaUYYSPvIeIc/+DscMoRHXDXbwEQSSoy3lEX/oqkWNmoDKGYx50E7I+jIkTr8bpdPLGG2+Ql5sDogaQUatVXHTRRURFRhAcHER2djavv/FmQO5NQUFBoTEIsizLLW2EwslxuVzodId/rPbu3UunTp2OaldSUuJPvxosaBglhxGHXolZUAgoMjKfkYMUHEXs+JktbU6TI8sSZcvewVmwC9EQSvh5jx23bdn855Bd1QgaA7LbDoC+/UiCu4+r11zuihwsK99FH9PJ/4DcWDI+uRbpeKJAENBFtiPp6tePfb2Vsf+/N6Aymml33bsAWNNXUTD/Nf/1mHPvILTHBac0R8HCt7GmLcU88Ab0bZqmurWrZB8VK99n0KBBrF+//qjrBoORxYsXMWTIEFavXo3FYuGiiy5qElsUFJoTq9WK2WymJPsAISFHVytv2rmriGqbgsViISTkzE0A0RQoTpCnCVqtlvfee4933n6Hc845+5giASAqKorMzEx+/vlnnnnmGf6oLiZaZWCcNxKNsoGkECAEBEJQU+GwtLQpzYIgiESeM53COQ8jnSSIO2TQTVRv+wHZ7UBQ69HGdqu3SIDD7lyGhB7IsowgNF7kHxIJIX0noovpjKgNwl2Zg+SyUb1nIc6SA6S/dznq4EjUwRFIHhcRgyZhajew0XM2FYJG63fpcpblULT0IxDVmHpfSdXW7yla8iHBKUPI+fVJJJedpAkvNzitqqfK994LjdhBqC/aqI7o2vQ9pkgAAbvdxjvvvMOQIUMYNmxYk9mhoKCgUB+UHYUzGKfTydtvv80jjzwCwETilaJuCgHjc3JwG0zEXn56rEifKlW7/8K67WcEjZ6Isc81yRz2/SupST3sXhM+4GoiB006aT9HyQFcFfkgeUGAkM6jADjw6c14bBVEjLoXfULdQGDZ66Hg5/uQvS6QofZ/ANBFtSPx8pmIWv1Rc0keF9k//IeQbmMI7XXYncqWl0re3BcwdRhC7Jh7G3H3PuxFGeT9/iyy2wGCSPxFjxCU1I/sXx7HkZ+GqDUgue0g+wqgGdoNpXzJawhqHRpzLK6yLP9Y2rAE2l3/fr3mLVzyIZZdCxA0RqIufh5BVJ280yngrSnDsmMO7oKj4y46duxIenp6k86voNDcKDsKpyfKEvMZjE6n4+GHH6Z79+4A/EA+O7HiVAKdFU4RNxIOJLQNTBl5OiJLHqy75tWKBAPGbvXfHWgoojGsznH5xu9xVeSdsE/Ob0+T/f2DFP79OoWL3qJw4VtkzLqO6syNaMJ8FZ9rMlYc1U9QqYmf+D4J18wiYfIs4id9QtyEdxENoThLMsn45JpjznfwqztxVeRSuupT8n5/BgBr+gpy5zyF7HFgy92JVJstqL54qstxV5cCULjwLSRnNYb2IxB1JvLmvkDGJ9fhyN+NKigCyWXDkDSIqIufx9h+BOVLfO5HsWPuwVWWDYA6NBHwpTr1OKrrZYMtexsAkRc80eQiAUA0hqOLSK49OrxrFBkZyQ8//NDk8ysotBSyICALYjO/GrYzm5eXx3XXXUdERARGo5E+ffqwefPmw/cgyzzzzDPEx8djMBg4++yzSU1NrTOG0+nknnvuITIykqCgIMaPH09ubm6dNhUVFVx//fWYzWbMZjPXX389lZWVjX5vmwJlR+FfgN1u57HHHuOtt97yn+uAke6YiOXoFUMFhZNRgYsfKCCkz1WYul3Y0uY0GZLbQcGPd/uPQ0fdj9oc16RzWtZ9irs0w5elw+vLcBY9ahqhPeu+z4WL3qFq32pkrwtBrSNsyC2IumBfoa+NX/t2CgC1KZqw4bejDWtbr/llWabw1weQat18gjsOx1GYjuR2IDmsddoGdxhKxICryf7+QdTmONwVvqJwKoOZ9rfMrtd8kiSR8cFVHLmjYex4DqaelyJ7nNRkLMdddgBdbHcM7YYiexyItVXBK1Z9iKt4L6bOo6jOWIPsdaM2xRA+5hFcRXupXPMxCCLmbmOIHDYFtT4Yl7UEbUiUfy5PTQVZ3z2Ep6YMQ/IQQvpdXS+7TwVZlrGs/xRnft3dBLVaw/Llyxg6dGiT26Cg0Nwc2lEozsls9lV9q9VKdGK7eu0oVFRU0Levr1L6HXfcQXR0NPv37yc5Odmf7viVV17hxRdfZPbs2XTq1IkXXniBFStWsHfvXkwm327JHXfcwdy5c5k9ezYRERE8+OCDlJeXs3nzZlQq32LE2LFjyc3N5ZNPPgFg6tSpJCcnM3fu3GMb1wIoQuFfRElJCYsWLeKF559nd1oaAOcQQSeCW9gyhdMNCZkvyMVjiib2khda2pwmw1pbK0EMjiJ0+D3HdMVpKiTJQ/k8X9C0qDHQYdo3/msHvpiGx3o4bWbkeY+iizqcr99ZtIfqPYswth+OPr4XQj2yLdWZ2+OkaufcY2a1MrYfjrv0AG5LPtGj76Z0xX+REYm5+Dk8VUWULn4VgA5Tv0HUGk44T8Hfb1KVfsRuh0pLSJ+r0Lftj1CPdIaOvO1Y1n8GgNoUQ3DPy9DGdEYQRGRZpnLtf3EVph7VTxUUTtsJM6nYOg9L6kJkr4egLucT1OW8es3bWDxVxVSu/S/e6ropTx966CH69OnD8OHDSUpKarL5FRRaEr9QyM1qGaHQJqleQmHGjBmsXr2alSuPXfRQlmXi4+OZPn2637Xb6XQSExPDK6+8wrRp07BYLERFRfHll19y9dW+xYf8/HwSExP5888/ueCCC0hLS6Nbt26sW7eOQYN8tXfWrVvHkCFD2LNnD507dw7gO9B4lGDmfxFRUVFcc801XHPNNezevZvu3buzlDIi0BKBtqXNUziNEBGIQ0e2vbKlTWlSBFVtTI9Ms4oEAFFUEzn+/yidOwPJbcdZnoM2LAGv3eoXCfETP/ClZ/2Hq4wupgu6mC6Nn1utw9z3Kry2cuxZGwgbOhVDUn8kuxXRYKZy/ee4LfkUL34PgKgLn0JlDPXFDtTiri5FF554zPHtRRk4Sw/WFQmCSPS4mQiq+v8s6RN6w+BbkGzl6BPPQtQdXvQQBIHQIbfirSnFkbsVvG4kjwPJbsWZv53M2bf725p6X4mx/Yh6z9tYyhYeO0OY1+tl1qxZCIKgCAUFhSbEaq27K6rT6epklAT4/fffueCCC5gwYQLLly8nISGBO++8k9tuuw2AzMxMCgsLOf/88+uMM2rUKNasWcO0adPYvHkzbre7Tpv4+Hh69OjBmjVruOCCC1i7di1ms9kvEgAGDx6M2WxmzZo1ilBQaFm6devG3r176dy5M8sp4wp87hTluMigBi0inQjGSNP76iqcngSjRpDsJ294GmNMHox1289INSU4C3ejie4EiPWqhxAoVEGReKuLyfrmXszdL8Ce71shN581GUHdtAI/bOithA2+2f/wrjKGAhDS50pUxjCqds1Fn3gW2nCfW5M6OBJENUieOpmDyrf+Rtn6bxC1QQiiCk9tPMIhoi9/o9Er+fr4nse9JggC6uAogrsc/rH2Omtw5m8HQNSZiBz7NILYPD+FanM8Hks+uoQ+SA4r7rIDAPTv3593330Xj8fD5MmTm8UWBYUWQxB8r+aeE0hMrLt48fTTT/PMM8/UOXfgwAE+/PBDHnjgAR577DE2bNjAvffei06nY8qUKRQW+iq1x8TE1OkXExNDVpYvmUJhYSFarZawsLCj2hzqX1hY6E9nfyTR0dH+Nq0BRSj8i+nUqRPPP/88Tz75JDuxUo6bPRwO/FtPJaMIpwvNm51AofUjIVGEA6/kwZ63A8M/MuqcKaiMYYSPuofy5e9StWE2ANr4XoT0v65R43mqSnCXHUBtjkNljkesxwOqrk0/bHsWADJV+9f4YweCOp3TKBsagiCIoDr6AV6lNxHS61JCel1a57ztwGqQPOjju6INiUbyuKjYNpey9d+ALIE2GMnjJqjLBYhaI+rQNmjCEpvU3eeflC97AwBtdGfCht/RbPMCRIx+GMvmb5HtFX6RAFBaWkppaSlms7lZ7VFQ+LeRk5NTx/Xon7sJ4Iub6t+/PzNn+nYA+/btS2pqKh9++CFTpkzxt/tn6ur6pLP+Z5tjtT/VtNiBRsl69C/nP//5D+2Sk1lDBXuoZty4ceTn5zNr1iwA0oSaFrZQoTWyjDJKcAPgyNvRwtY0LYaE3kSOeQRTj0sAcBXvbdQ4ruJ0Kpe+Ss2On7GsfI/yeY9RteW7Om2OlS3I2OlcIse/gmiMOBxg3OX8VvVDcgh1aBsAHPlpuKzFZP/wH8rWfQWyTHC3sURe+BRRFz1LcLexGDuMQhvZHkHVvG6PUk05AOYBU07SMvBUpy3AkbUeZ3Hd1Kf33XcfmZmZzW6PgsK/jZCQkDqvYwmFuLg4unXrVudc165dyc72ZVWLjY0FOGrVv7i42L/LEBsbi8vloqKi4oRtioqKjpq/pKTkqN2KlkQRCv9ydDodW7dtY8aMGXz77bfMnTuXuLg4br31VpKTkymWnXhQ4t0VfEhIVODCcIRL2skCVs8EdNEdCel1KerQBPA4kTyOBo9h2/M3AMaE7hjifL6nzrytVCx/m9K5Myj9/WHK5z2Gbf+xA+hCBkzh0Fe2KqhhhcSaC11UB0S9b7Xu4BfTcJVnY0geQvRlrxHU5dSqJgcMAXQJvRF1QS0w+T++S2tjYGJjY+nQoUML2KOg0AIIYsu86smwYcPYu7fuglB6ero/fqhdu3bExsaycOFC/3WXy8Xy5cv9GcvOOussNBpNnTYFBQXs2rXL32bIkCFYLBY2bNjgb7N+/XosFkurynymCAUFzGYzL730EpMm1S3sdChSvwxXS5il0MrYSAWzyOEHCtjB4erEXltlyxnVzAR1PBuA8j+fovT3h7GsnYWnqqR+nWs3AOLOu4d2k17F3G00yDJeSx5qgxlNiG8FyVWw65jd1eY4gvv5aht4bRXHbNMaCBt8M9rIwxmYdPE9m6UuQX1RmWJx5m2nbMnryB5ns84d3LVuitvBA85i4MCBLF26lOBgJfucgkJr4P7772fdunXMnDmTjIwMvvnmGz755BPuuusuwOcuNH36dGbOnMmvv/7Krl27uPHGGzEajf4YI7PZzC233MKDDz7I4sWL2bp1K9dddx09e/ZkzJgxgG+X4sILL+S2225j3bp1rFu3jttuu41x48a1mkBmUGIUFE5Ajx6+YlorKac3JjoqaVT/1RykbuCyyhCC11Hlz9cPPteZ6t3zEUQNpm6tZAU5gAS1H4Ft3wrclb56Ae6SfVQufRVDx9EIGj2ayPZoat1vPJYCrOv+i+SsAkGFaPD5n7vKc9CFxRM1eBLIEhFnXYYuMglBEMn6+UnsBcd3bXIX7QYguPOYJr7TxqOP74E+vgeusgOULJhJ5ZpPCD/3If/70tKED7+LkgXP4qnMoXLdZ4QOm9asblya8CTc5b6Ax7/++guTyYRKpWLDhg1s2LCRKVOuVyrHKpzRHCqC1txz1pcBAwbw66+/8uijj/Lcc8/Rrl073nrrLa699lp/m4cffhi73c6dd95JRUUFgwYN4u+///bXUAB48803UavVTJw4EbvdzujRo5k9e7a/hgLA119/zb333uvPjjR+/Hjee++9ANxx4FDqKCgcl5qaGl566SVefPFFAEYSTgJ6QtC0sGUKLcEKykijGk1INO4jcvibel1GSA9fteLS5e/izNsOgkDCNbNaytQmx56zBVfZQVRBYVTtnAeCgFSbKjZk2O14LQXU7PrtqH66iERSrn/3uMG7eQvewrJ7CaFjHkX9jyrNAJZ1/8NdvJeYS/8PdSt1PzoSy445VO+ah8oYTuSFT7W0OX4kj4Oyha8g2SsI7nEJQZ1GN9vcXlsFpfOfBSAyKorhw4bx66+/snPnTt7/4EPef+/dOg8SCgpnCofqKBQV5LdIHYWYuPh61VFQqIuyo6BwXIKCgnjhhRe4+uqrGTJ4CCts5WhFFZdK0YShYSsW8gQnA2SzUuH5X0C+4AIZIgdNRKULxpjQjfSPb8BVvA8AV9lBn0gAkGXyf7qP2CvebNZUoieibPXHyE4bQR1G4ihMw12RhcpgJqjTuehju518gFpkScJ2YBWOvB1owtqiCUvEeYS7kHX1Rwi1fvoqo5n2172Ddd9qZEkivM/FJ8zwI3t8AeK21LnIbgemfpMR9cFHXPe5ytgOrCKk5/gG3X9LYO51Gbb9q+ukSm0NiGo9ERc8SemfT2I/sBpjx3ObZVehZu8iqlPnARAVFU1JSTGbNm2irKyMnj178tGHHzS5DQoKCgoNQREKCielZ8+eVFoqyc/Pp127dvxIAUZBjU32gAzLVTJXe2Nb2kyFRiIhcxA7IaiJPEHhPScSCCKh3c/zP1QZYjviLDuA21JAycKXAUi66kXKt/1BVcYain+fQfT4l1tcLEguG46sjQA4j6jU6wacRXuJn/DuSceQZZny5e/gyN8J+IqxuSuyj93W4Svqk3Tl86iDwgjvM65edpraD6T64GZ/nIJt3xKCjxAEnvKDvvtxVh+re6tDkjxIDgua0ISWNuUoRFFE1Ab5qiRLbmiG7Etq8+H3YdKkq1m3bh1vvfUWERERTT63gkKroYHBxQGbU6FRKO+cQr1Qq9W0bduWAwcO8NBDD3HRlZfx8MMPEx0djVZufWkaFerPSqGChZTwMwWkHRGk/E+MsgCyhKe67PC5xF5IHieV678AyUN430sISuxJm4v/Q9Sw6/DayrFs/LI5buOEVKUtACA4ZSBtxs2g7eVP+6+F9LmiXmPUpC/Gkb8TQaXB2KYHHW/+L7Gj7zhusa6w3hehj0xukJ3mLqPoNO0LTB18GS8cmatxFqb5rws6n/+ryhDaoHFbCslhBVnGWbALd0VOS5tzFGLt++m1W5plPl1sVwwpwwFITk5m0ODBPP5k63HJUlBQUPgnilBQaBBJSUm8+uqr/Pjjj5x//vkUFxfTXjIgI2PBjROJ9VSQia2lTVWoBzIy+2Tf6nRUVBRrqGAHVlxIWPHwBbn8jC9XdG9CAIGS9d/XGQFZQpZ9+f/D+/kKcAmiisizLkdjisK2fyX23G3NeFdHE9TxHBBEdBGJhHQcSlDbPgCogqMwth9VrzG8NT6B1P6G90meMBN1cBjhvcbS8Zb/oQ1rQ0jnkUQN9mUl0obGETPy5kbZKqq16KPb1R7JVG//CQBnYRqy0yfkdA1wlWpJ1MZwgrv6gtqt/6gZ0RrQxnYF8AcXNwVeu5XSxa/hKNjtK6Sk9rlpfvbZZ7z37rsc2J/RZHMrKLRGZEFokZdC41CEgkKjObRdvpYKPiGb78hnNjlsw8oiSlvYurp4kSnBiXyG1YSowsNGKpknFLORSuTa/3PirVf/DVTiRWbMmDG89NJLeJBZSwW/UMgWKrHjpRQnGdTQiWC0CFRlrPX3D+nkWx11l/mKRamOqKkgqDQkXPwwAOUr3kPyHF1MrDmoyViJPWsDIGPPq12dr92G9laXUPTbw0iSdNJxNBG+h/eMT6ey/8t7/H3UwWF0uPED2lz0EFFDrqHb/b/T4aaPEdWNd2VR6Q9nzpDdvmxTjszV/nMlC15APkZxttaIIXkwwCm9H02FXOvCZd30FSV/PY1120/IUv3+7dSXqtTf8Vpyqdr2A9bN32JLXwRA586d2b59O0sWLw7ofAoKCgqBRIlRUGg0ffr0YdWqVUyfPp3SkhKumTyZX375hb179yIDu6miGyZq8FCEkxSav8CRjMw+atiOlXLchKElDDVeZFIwkoQBHadfhpEinOyjhnTRhlavx+X2kOe2c0Cw4xRl7F43AwglET1RHDuQtAQn2/D50s+YMYPRo0fTrl07Fi1axEsvvYSltvIy+DIebaQSFxLmdgP85w3R7TF1GOIXD4dWSw/hdRz2pfdY8tBGJAXsPagPFRu+wJaxovZIILyvL1ZAEAQ63PQRGZ/djmSvxF1+EF1kygnH0sd2w5A8CPvB9ThLs6jY+jvO8lyCkvpgrhVMgSKs11g0wZHk/P4CSB5Kf/cJLmNib2w5voBxr92COqh1+7aXr56FPWczgkpLUM9LW9qcOjgL07BlLPMfS3YL9gOrUOnNBHU5L2DzuAp8KW0ljwtH9uHCSnfffTe9evUK2DwKCqcLsux7NfecCo1DEQoKp8SwYcPYuHGj//iFF17AarUyefJk5v/1F8Go+QtfKs2r0RKKhuVCOUWygzA0nEskKhq+JSgjk4WdnUI1IbKKvoQclbZVRmYxpezHhiAI9OjeA6/HQ9qePQBkYccoqjlLCiEcLSU4ScJQZxwnEsU4aYMeoRF2NgWFOPiNImJjYpgxdToPP/wwer2e9957j5UrV9K1a1defPFFNlLJRmAQofTBfNQ4+2vdwxYuXMjo0b70kOeeey6DBw9mw4YNLF68GFEXhDYkGkdJJm686KNSiB52XZ1x2lz8MOmzbkZjikBU1/1vULnjLwCCOo9pdpFQufFrbBkrCErqhyG2AyGdRqKPbOu/rjKG+v8+MhORp6aM8lUfgSwT3PV8jEkDARB1wYQPvQ1HyjDKlrxB0YpPffPs+htnaTbRQycHzHZBEDC1H0jsOdOoTF2M116Jx2YhatAEcooykFy2Vi8SAOxZ6wEIO+dBNGGJLWzNYVwlGTjzfYJLE9mB6AsepnLjt9SkLw3orqNt/0pkd60bZu3/LykpITQ0FLVa+flVUFBo/SjfVAoBRRRFQkND+frrr4mIiOAv+XC+/XVU0A0Te2Sfn3UFbjLJZgThdCAIzTE84WRk7EgYUeFBIgMbVXjIw0ERTpAhH8hSObncG43piI90KlXsx8bLL7/MI4884htPllm5ciXt2rVj165dTJwwkZU15f4+a6jgHCJIwkgZLuZTjBsZg6BivBxDaCuoIZEqVBMTGU1uXl6dfOvTp09n+vTpANx6663MmTOH//znP6z3VOJEoj+hxxRl3333nb9SJIDRaOTvv/9GbzBg7nsJxviu1GRvJ2rwJAS19qj0noKoovO0z5Hlo913zF3PoerABjxVxUdda2qcpT7f76A2PYgceNVR18s2/er7Q1SjjUg+fH7ZO3gseQBUrP4Ej7WIkJ6X+K/rYroQOugGPJYC9Am9KFv+LqXrvyNy8ETE4wQ2N5bwPhcT3ufiOuckVw0AXocVlb515gOv3rsYy+ZvAVCHxLWYSHAWpiF77Ojb9EOWPLiK9lK9ZwGe2mxVKmMYEUNvRK0PwZ6zFUGjx5gSmN0h2evCtn+F/zgsPJy//vyTyMjIgIyvoKCg0BwoMQoKTUJYWBiXXlrX1SALu393wel08r///Y/o6BhWUM6PqiJseHEjUYGLCtzU1Prff0kuP1PA/8hhOWVswYI7zMhdd91Feno627Ztw+51k3pExp5qPOxW2ejXt69fJIBvpXbkyJEkJiYyduxYSstKWb9+PV9++SVbt26lY4cOLKWMORQyr1YkTJgwAbvs5Xvyyf1HdeLmxIqb5ZSxHxuPPfH4CYsyJScnM336dFwuF9dffz3bsPKDqpBUqnDUvs9OfA/2MTExR/V3uVy4XS7sWVsITupLzIgbETX6E9YAONY1U8ehmLudizN/B5Ubv6Z63zKq9y2l4Kd7KF3yRiPehfoTNvAGAIrXfn3s673GojKYQZYoXfImpYtfJ/+ne/FY8jC1H0TipU8CULNvaZ1+giAS1H4E5n4T0cV0QR/fE4DSdd8fNUdToQ6JRdS2zkrpjsI0v0jQJfQhdPgdzW6Ds2gPRb8+QOWaj7Fs+AJnQSrlS16jcu2sWpEgkDDpA9pc8yG6qA4AqE3RyG4HzsLdAbGh+LeH8VaXAKDT6fnyiy8YNGhQQMZWUDidkWS5RV4KjUPZUVBoMt555x0MBgMmk4mZM2fy+eefs3nzZqZPn45Wq+Xmm2/muuuuY8WKFVwybhxfenOPO1Zc906Upu7CYDCwYMECRowY4b8myzKhZjNWi8+nvhQXc8ViRLWa2Z9/fkIbdTodAwcOZOBAn3tJ6u7dTJ48mZ9+8mWaWbRoEaNHj+a+++7jnXfe4Q+KmUbzutA48LJSqOAgNiIjInnpoSe4884769VXEAQ+++wzrrjiCh75z8OsytjHKsrRIOKuFQr9+/c/qp9er2fAgAFs3LgRyeM+yqWovgiCQGT/K7DsXnLUA7ezcDeSx9VkQa6qWtec6KHXHfO6Jjic9je8T+7cl7DlpYKoQh/eBmObXkSPuMEfLCw5q6netxRddBc05jgAZMmD127xrVjXBr9WH9xM9NBrm+RejkRQaZAcVQitpJDdP3EVpwMQOnQautqsQs1N5eqPANBGtMNVnk3lWl+VcEPyICJHTANRfdTnLmbsY+R+PRVH1noMbY/+N9EQjtxJQFDhdDpavJaIgoKCQmMQZFmRWQotz6JFi/j555/RarUsXLiQQYMGsWjRIoYMGcKsWbMwm804nU60Wu1RFVQlSUKlUtGHEAYRxm8UIsSGsXHTJhISGlfoac+ePVitVr+AKCsro02bNgguD1Ok5ike5cDLLqpIU9nQBBl4ceZMbrzxRoKCGhcULssyq1atYuTIkQD8/fffjB49+rgPMK+88gozZswg4cIHMMR3QRMS0+jqtTW5qWT9+CgAod3HoItIpGjFZ6iCIom99OVGjXkiPFVFlCx4EdnrJuX6d9CFHf+/mSzL2PPT0IbFoz4ibgFg74fX4nUc3qlSBUWiMoTgKj1w1DhJV75AUNumD07N+GwarsoCjClDCRvcuBSsTUnhbzPw2iuIGP0watPRu1VNibNoD7b0JbhK0lEFhdNm0gd4qkooXvh/qIIiiLlgxgn7F857GmfRXiLHPuvbbapFlry4ivci6kwndaNy5GzBsvELAH788UeWLVtGamoqf/75JwaD4YR9FRTOZKxWK2azmdyCQkJCmtdt0mq10iYuFovF0uxzn+4oQkHhtGfu3LmMHz+ec4hAi8gCSnjuued48sknAzbHwYMHadeuHe0JYgxN42PsRiILO45a96tMlRNZLTLlhhuYMWMG7dq1O/kg9WD16tXodLpj7iQcSWFhIYlt2+JxH85+FNH/CqKHXougatgOgyx5KVn3LcHt+mOM6wLA7jd9FYfNA64luOM5DbyL4yO5bBT8dB+CSk3by58hKLFno8c6+MOj2PLTiB4+heLVX4LkBUFE1OgxpQwgtMf5SC4HQe36Bjw+4Xh4HNWkf3QdgkpN3IT3Gy3emgLLtp+p3v0Xpt5XYWwf2ExQJ8NVso+Kle8DIKh1xF3+f2hCGiZULNt/p3LTN5gHTkHfpt/h8xu/xJGzGeAoEXEkkquGihXv4bEWsGvXLl544UXGjBlNeEQEBr2eCy+8sJF3p6Bw+qMIhdMTxfVI4bTnzTffBKADQfxWWxzsuuuO7W7SWKqrfWk+a/CQix0PMiGoCUMTkGxIHmT+UpVR4PVlRomMiOCOG6by0EMPERcXd8rjH8mwYcPq1S42NpZpU6fy/vvv+8+VbfoFlS6IyIETGjSnIKqOcgFqe8WzZP/yNNWpfwVUKBT/9RwgE97v0pOKBI/dikpvOubDttdR7XNJAiL7X0F4r4twlB5EH9upRd1ILGnLQJYQNcZWJRIAbAfWAGBIGdrsc0uuw/FDbW84scvh8RANvgeImr1LAAFdXHeqtv/qFwngy2Rk6jHumP2dRXvwWAu47bbb6N69OzfeeAMRERFs2rSJ888/v1E2KSgoKLQkilBQOGPwIKMV1SC5iIqKCujYPXr04KmnnuK5557jDw5n8BlKGD05tdUJLzJLhDLKVR4Wzl/I8OHD0ev1J+/YDNx3331s2LiJqydO4NprryUuLg5XZX5Axg5O6ovGHIPbUoTbUuD3/28MksdD2ZLX8VQXITmsRPS/kugh1/yjjYusn59E1OhJHP8E9vzdZP3yNPro9sSec5t/p0OWZSy7l1C5ewngC8gGELV6jPFdGm3jqVK87jtK134DgDo0gYgRd7WYLcdDqq0cLXtceKqKUIclnjAAPpDo4nuiCorEW1OKx2ZBbTz2qv8Jqc3c5bHkYtlQV2yEDb2FijWf4sjZhKnHODzVJbiK03EW7AJZwtT7CoTaXSW73Sda0tLSuP/++3nhhRdISTlxnQ4FhX8Lkux7NfecCo1DEQoKpz133HEHS5cu5W9KiJC05ALLli1j3Lhjr/o1lmeffZZbbrmF4uJizGYzt9x8M9vWbaCbx9SoWhDgEwkLxVLyRBdzfplTJ01pa6Bjx45sWL/OfxwWHoGz/PhB5w0l/rx7yfrpccpXfUjMxc81epyqHb/gKt0HQFBiz1r3qLpfb5a0pdjzfZWZMz6biqe6DABH0T6yfnoSjSkCV8XRIihqSNMHKJ8Ml6XILxK0UR0x952A2hTdwlYdjSo4Cm9VESVzfbEA5kE3o09onqJigiCgb9OXmr0LcZdlojb2afAYps7nIggqvPZKag6swWuvxNCmL5EjbwfAsvUnJHslRb9MP6qvddsvuEv2AjBq1CjAl6Z43bp1XHnllY2+LwUFBYWWRBEKCqc9EyZMYOLEifzwww/k4aBH9+7+gN1A07ZtW9q29RXtGjJ0KCtXraIAB21oXJDiWirIFZz8+cefp4Vrgk6rwWa3BGy8oMSemNoPpmr/Oixbvsfc7+pGjePI3wmiio63/BdN8NGFyGqyt1O45GPURjPGxD5Y9y4HIKL/lejCEyla+SmuioLa1gIRZ11GRP8rcVkK0Ee0bKGwqv0byZk3EwBz/2sJ7hQ4N61AEz70NirWz8ZbVYzsdSGIzVv1XDSGA1CV9jeGxD6NGiO4k+8h39z76ErSceNfJO/7u2uPBPRteuPI3QbgFwk9evTg5pt9QebBwcF89913jbJDQeFMRZZlmjs8VgnHbTyKUFA4I5g9ezbVVitOt5vZs2c3S7DSrz//QoSgI15unJtQIU5SqeL9d94/LUQCgMfrRR0S2KxPoT3Pp2r/Oqr3LCSo02jUwQ0PFlcFR+GtKUUdFH7Utar968n5/UVEjZ7kyW+iNUVS1f1c1AYzhmifO0ho93MBXwYtPA5ErREAtbFlg94kl43cP18ByYs+8SwcuVupTpuP7HagjepIxKi7Tz5IM6KNSCbmomfI/+FuQEAb3anZ5pZliZo980FUETrgmpN3aATq4EiSbjn84O+1VZL77e112vz0009KKlQFBYUzBkUoKJwRGAwG/vjrr2ads6i4iFBZRGyE25EdL2m1BeKuuuroqsGtlfYp7dm4eQuSx4mo1gVkTFO7/kQOmkjp+h/8D+gNxWuv9PuHH4ksS+T8/iIA4X3GoTX5RIgpqe8xxxFFERppQ1OQNed5ZI8LoE5ALYAjbxvVexYR3KV1uasByB7Hob+afC5X6X5cxenU7FkAgKnHxWibqRK0yhiKPq47jgJf0Ps999xDp07NJ44UFE5HlBiF0wtl2UNBoZHc/8ADZGMnHweFOEinmr8p4Rshn11YyaCGbOzY8ZKHg91UsZFKllLKj6oisnUerp44MeCB101Jt25dkTwuCECmpyPRR/uq4xb++iAeW2WD+8suG+qgsGNkARJQ17oiVeyYf4pWNj/xY+6G2nsydz2biLMup9Od35F05QsAWLa0TrcWQ5KvAnHVtp+bdJ7qtPlUrHjXJxIEEVOPiwgfdH2TznkIWZYpX/e5TySIamY8+ijvvPNOq8tEpaCgoHAqKDsKCgqNZMKECTz77LPMpch/LuX/27vv8KiqrY/j3ynpvYdQQ5EOShURG0oRFSsqCooFEUWx4+u9ihXRq14FxXJtYG9YASkqIEiRXkOHQHrvZWbO+0dgNA4lCclMEn6f55nnJufsc84avMqsWXvvFR/POZ06MWfOHJc5kRazmeioKJo0iWP8wAt46KGHiIlxb0Oqk3Vkq9bannse3PZMInpdSeaf35D67YPEXPZ8taYgeYU1p+TQBkrS9+Ib9Ve/CZPJRLvb3mPba1dhLyuq1ZjdwSe8KR3GfwqYMXv/NcVt10+136SuNgX3vJbi/Ssxajmh/LvS5C0UbvsZ78jWRJxzZ51XEQyHHVt+GtmrPsbiH4p3WHPyt1RUMZuDowwAAGTeSURBVLt16+q23Z1ERNxJiYJIDbVu3ZqHHnqIFi1acM4559C8eXPCwsIAOHjwIAEBAaSkpLBx40Y6depEx44dsVob9r9yR9Z+GLYyTN6122U2ZsDNFKfspOjgJlK/n0TkhQ/jU8U57mF9biL52wfJWPUVzYY9VOmco7QAHLajNokrSd+HvayYgKYda+U91IV/TscqTNyEo6TA+bujtACzT6C7wzquwm0LAPBr1afOnlG8fxWYLcQMewqzG/69Svn2EcqyXXf8GjJkCHPdPO1RpKHTTKCGo2F/ahHxID8/P1544YWjnmvWrBkAYWFhdOxYfz+EVteRKonDXo65hjs9HU/MgJvZ++kD1b7O7BuINSSOgn1rMBz2ShUPi28QmC1wOPay3BQOzvkPGA5KUncBcNodM7H6h9bKe6hr+796rPKBanbJdgfvyIpF4iUH1+MV0gyT1bvWn2HLSaxoPOem5LsiSTDx10ecip/z8vLc8nwREU9QrVREqiwuLg6AslrspfB3Zp+/vj2vajXBOT6yDY6yIspykl3OBbc7C8NhI2nh6+x6fxwlKTucSQLAjrdGk7/3z5oH7kZHqgcR591L05H/q7VF5bXJJ6Y9JqsPxbuXkPbDo5SmJdT6MyzBsWA4KnaqqmOG3QYmC2Ac/l84kjAkHqybfxdEGqsji5nd/ZKaUaIgIlWWn1+xU5NvTLs6ub/FN8g5RShzyfRqXVuWuReA8rw0l3PRZ92ANTCCnE0/g+EgtPOFdLznazrc9Tkx590OwKG5L59k9O4RfLhTdHn2AQ9Hcmxmb39iL38R3+Y9wLBjz0up9Wf4xnUHIOPXV2v93v9ksliJOLvi/ycYdgAiI6OIio7mumtr1vtDRKQh0NQjEamy7du3Y/HywWytm+kuVr9g2t7yNim/vkX+rhVk/PpfIs+f6DLOUVZCzp8fU557kPD+4yhMWEh51j7M3n4EHGXrU+/QONrd8g7lBRmU56biF9cRk8ULk8WLiDMupXD/Ogr2/klp9iF8wmq3T0Rty9myEADf5j2rNL4scx8YDrzCmpO79gvKs/cT2mc01uDYo24pW1vM3v7Yi7IBsAY3qdV7O0oLKnomULHjlVv8Yzej+++/j379+nHeeecB8PHHH5ObX8Adt9+GxeLeRnMiInVFiYKIVElpaSnTp0/HJ6JFnT7HKzCC0C6DyN+1gtLD+9P/Xd7WueRvmA1GxZSTtB//mrMf1n3YMbenNFmseIfE4h0SW+l4adZBCg5POzJ71ax5nruUpO/92/v+F02umX7cmEtTt5Ox6D8ux9PmTAYg/Jy78Wt2el2EisNhozxzH9bgWLyi2tbqvfPWfIq9KBuzTyBhbtoO1b9VXzKXzHD+/tNPPzFhwgTn71arlbVr1uC49RYlCiLHoc7MDYumHolIlfz2228AGLbSOn+W1S/k8E8GGb+8jMNRMRc9bd7T5K//Gt+I5jS/7F+YDn9IjjrrBjreO5uYs0dX+1k5WxcBEH/Df/E63HOhvvKNisc7/K9tQI8s2naUF5P526uk/vQEJUmbACjPS/lbkmACTJi8fAk9fTg+kRVbyBbtWVZnseZvmA0Y2PJSSJt9P8UHamcNiGEYlKZUJJBNR0zHO7xuE9cjsv54H4CWLVtyzz33sHr1ap555hnn+WuvvZb/vfM2Xl71b3G5iEhNqaIgIlUydOjQih/qcLrKEX6x7Wh3+/skL3ydgr1/kvzZ2IqpH4ZBRM8riD57NCazhbY3vU5ZTgr+zbrUqNGVrTiPzNVfg9mCX3TrOngnta/tTa+T+OPz5O9cTu76rwnqPIz8jd86E4TM314lrN+t5G38FoCIM28kotdVle5hK8phz3tjsAbVXR8Ps09wpd/tBem1cl+TyVSx05O9nIJdiwnuNLhW7ns8RQfWULhzCZi9mDVrFs9NmULLVq25/fbb6/zZIo2N4/DL3c+UmlFFQUSq5EjptuVVT7nleV6BEUT1GwmANSgS/7hORPcfTcw5Y5zfpHsFRRHQvGuNu+Ha8jMq7l/PKwn/ZDq8805hwkJSvrmPwl2LsfgFO9ccZP/xLvbCTKLPud0lSQBI/e1NAHzraNoRQFCnwZh9gwAI7HwJAR1r5wN9acpWsJdjDYoh8LSBtXLPE7HlplYkqg4bU194kScnT6Z3rx6Eh4e75fnHU1JSctxzn3zyCWVlZW6MSEQaE1UUROSEysrKGD16NDNnzqRg31rCutb9t7gAfjFt6TjxuxonAieStWEOAC0u+1ed3L8uOGzl5O1Y6nI8tNswAlr3JX3J/yhJ2Y53eAtCu1189HuUFgKQvfwdDFspkRc8gFcddDY27Ha8o9sT0P7Ck76Xo7SAvDWfVCQKZgsxQ//lth4KQV2G4hXRgrS5z+Dr4826detYu249u3btonfv3m6J4Wj27t1LaWkpERERREVFuZx/9913eexfj5OTk8P48eM9EKGIK8NwtrVx6zOlZpQoiMgJ3Xb77cyaORMAe7F7G0zVVZJgLy0iZ/N8AHyjWtXJM2pLzpZFpC55D3tJPmafAAAi+9+EvSSf7HXfYbZ4E9RuAN6hTWh+xdMnvF9U/5tJ/HoS9sJMoGJr2dpOFIoPrMEoL8I7pnYaDpYcXEtpylYsgZHEDP0X1iDXD8Z1xWQyUZK0GYvFwtSpU2nTpg1jxozB27v2G8lVhd1uZ8aMGaSmpjJu3DgiIo5eERswYABXXjH8r2mDIiLVpERBRI5r+/btzJo5E78mHQhocTrhPS7zdEi1InvDTwBY/IJPMNJzDMMge+NcUn5503nMUVoIZivhZ1wOQFS/6u/64xvdhoDWZ1Kw83dMFm/8W59dWyE75W2cjcnqi398v5O+l2EYFO1aAkBA23PwCo49wRW1qzRjD3kbvsVkMvHzzz8zcuRIQkND3RrD3/3yyy/OHZfuvPNOzOajzyLu1q0b7733njtDE5FGRomCiBzVww8/zFtvvU1eXi5mixdxgyfiExbn6bBqja0oFwDv8GZ19gzDbiNv1wpyt/9GWJeLCGrTt8rX5u9eSeL3z1Y65t/idMzeAYR1v+SkYyvPSwUgZvhUTMf4oHkyzN5B2PLTcZSXYDnJ7tGOoizshRkV773niFqKsOqylrwBJhMGJu666y7uuusuHA5HrVW7li1bTmhoCJ07d67S+PPPP5933nmH0tJSmjSp3R4VInXNE52S1Zm55pQoiIgLh8PBhx/OJC+v4sN0VP9RjSpJAAjrfjFZ676n+NBWtk27mna3/g+rf2itPiP5lzed05sK9q0l/tqp+MUevat1SfpeDs19CWtAOLEX3OGSJAD4RMQT1b/6W8Aejfnwh/eT6R1Rnp1I7rovsBflED30cWdXbQDf5mdQlrGTsoxd+FWxOdwxn5NzEICgzu6bQmM47OSu+5rc9d/8/ajzp+XLl9O/f/9aeZa3t1e19nm3Wq3cdttttfJsEZHj0a5HIuIiNTWVtLRU/Jt3o+2Yt4joebmnQ6p1PmFxNLtkEgCGrYzi5IRavX9x2m5yNs/H7BtMsyueAYeNvZ8+gP3wQuK/y1zzLXs+upfSzAMUHljP7g/uBMDsG0xwh/MJan8efs27EXZ4utHJKstPpyR1B17hLSt9uK8OW14qaXOfpDRlG7a8ZFK+fZii/asw7OWUZe0nb90XANgLMpzXlKZsJWfFexTtXlK9eFO3ARB8+pU1irUmDrx/wz+ShMrat29fa8+KiYmpFzsoibjDkYZr7n5JzaiiICJO6enp5ObmMnJkxbakEWdcgndo453aENzuLFrf8F/2fPogid8/S2jnC4m9YBxm68kvUrUc3hrUJ6IFvk06YvYJxFFawKE5/8FksRJ+xqUENO8GQGFiRQ+EoPbn4RvdhvTf3wPDIKz7JUT0vuakY/m7suxD7P/8fgy7jZAe11X7+vKcg87OzgBB3a+iMGEhjpJcspe9TfY/xhdum4stN4nyzN04SgsAKE3aSHnmPoJ734jJdPzvqwzDoHjfCoBjzsWvTbbCLEr+0RH8iiuuYPbs2QB4+/jy1ZdfEBkZWe17l5eX8+GHH2K327ntttucHZxbtHBP0zgRkepSoiAiQMUHsv5nD2Dnjopv1mPOuYWgNmd6OKq65xvdmhaXP86Bbx4nZ8tCHOUlNBv2cI3vV5yyk7KcJDJWfuE8ZjabaT3mXXa9eS0F+9YAFWsQml/+BPaSvIpjZgtNLrq3IqbYDmSs+IiQzhed3Jv7B4fDQfL8VzBsZUQOfBCf6KNPgwKwF+dSvH81Zp8ATBYvfJv1wGQ2Y/tbhcC/zQAC2w8ksP1ASlMTyFnxHiYvfxyl+fi17EPJwbU4SvIqOik7bJi8/Ym84CEyFkyh5OBa/Fr3xzuyzXFjzl/3OQAW/7r/xr08L4WkLye6HJ8772cAvvrqK84880yaNm1ao/uPGXMLH3/8EQDLli3jww8/rLNdvUREaoMSBREBYOnSpezckYBXUBTRA8YQ0r72d8KprwKadfnrlxN8w30shmGQs3k+yQtfP3IjLAHhNBn8EABmqzfhvUeQn7AEk5cvZZn7SPz2Secz/Zt3d97LL6YtzYdPrlEcx5O66DVK03fjFdaC3LVf4CjJx7dFT0J7ulYWCrbPp2Dbz87fzT5BeIXEEdL7RnybnUHJwXWU5yQ5z/vEtCdm+NRK9/CLPwtHcS6+cX8t0i1N24Fhr2gAZvELPWHMxYkViVXcNa9V673WhMX36DtglRQXARAcHFztJCErK4sPPviAOXPmsGjRIsxNeuHI3s2sWbOYM2cOd999N5MnTz7Z0EUaDHVmbliUKIgIALfccisAbce8hclyav2nIfnw9qPeYU2JG3I/AOUFmZSk7iKwde9jTo8xHHZK0vZQlptM1prvKE7didk3iJhzxxLQshdm78oLhSP7Xk9k3+sBOPjjs5Rl7CWq/80EtXNPUla4dzWYTJTnJYO9vOJYwkKCOg5y+ca+OHEtmK34xZ9JafJWAErTEsj89RXCzrqNkoPr8Ao7/o5R3mHN4G9jig/8Sc6Kiu06zb4hmLwDThz04TgdxdmY67h3QlnWfufPJrOFQRddyI8//ojJZCIhIYGOHavXE+LXX3/lsuHDKSgowBzYBHOTnpijO2OO7oKRd4DMQ6t58sknad26NaNH184idRGR2nRqfRoQkWMqKCwkqN1Zp1ySAGD1DwHANyoes9lM4cHNHJj9JIatFLOXL/7NOtP04oewePtXui550RvkbF4AgNnbj7AzriCi341Vmkvf7JLHav+NnIDZyxdHWRHYy2l2xTOkL/+Q0tSdlGUn4ve3RMFenIu9IB1LYBShPUc6j6f+8H/Yi7LIXVsxrcooK67ys4sPbSBnxftgMhHc8wZ8m/c48foEu835c86GbwnucjFW/wgcZQVYA6u/RuCYz3E4yPz9TQLa/JWwjbjmap555hmsh7s/d+rUqVr3tNlsjLvzTors3lg7XuOSFJlCWmIUpuNI28SiRb8oUZBThoEHOjO793GNyqn3iUBEXBw6dIjUlGRISaYsJ7lRL2D+p9Ksg2Su+RaLXzBxQx/EMAwO/vQCGAa+cZ2x5aVQsHcNyQvfILz7xfjFdXTOK/eNjAcgZuAEQjpe4Mm3USWR/UaTtvR/BLY+E/+mnfEJa05p6k4cpQXYS/KwF+dgDYzGcFR8QDdsZZWujzjvXtLnPkV51j5M3v74tqp6X4iCzT8ABpGD/o0l4OidhAHKsw9gDYrBZPWhcMdC5/HChEUUJixy/u7Xqi/RA++r8vOPpyRlC4U7l1C486/dmKZNm0ZUVM0rGLNmzWJHQgKWdsOOWjkxygpwpG3izDP7cf/9tfM+RERqmxIFkVPcxo0b6d69Yn58YItuWPxCPByRe2Wt/R7DbqPViOcpSdlB4ndPYy/JJ7jD+cReeA8A+794iLyEJeQlLMEv9jR8o1rj1+Q0crb9AiYzAfF9PPwuqia4w7kEdzjX+XtQ+3PI2/4LxftXVXzb/3cmEwGnDax0yBoUU7EOweJd5Z2hHLYyChMWYstNwhrcxCVJMOzllKZux16YQcGm74CKbWHDL3iQstTtAISfNxF7QQa5f37kvK5430psJQVYfQNxlJW4TPOqDpP5ry1i//e//9GjR4+TShIMw2DKlOcxBcVhDog++pjiiv2hxo27A39//6OOERHxNCUKIqe4pKSKBaktrniCwFYn1xiroSnPzyB70zwAdn84HgCTxYvI/jcT2v1S57iWI17EVlJA5spPydsyn+KUHYevMxF6+mVYfQM9Ef5Jy09YDEBp8hbARGDbfhTsWg5A8BnXEtD2HJdrzD7Ve68FW36iMKFielbQ6VcDYBgOSg+up2j3Ysr/vi7A6g2YcZQWkPP7DGx5yQB4RbbFJ/o0/FufhaO8hKJdi8nf9B2HPv6r6Vjs5c/jE9GqSjGlzHkKi9Ubv7bnkvnrq/i1+Ov/9yNGjCAoKKha7/HvysvL6d//bHbu3IGl1fnHHGc6vHA6JiaGdu2OvvtUQkICZWVltGnTRsmENBoOw8Dh5rlH7n5eY6JEQeQU5+NT0aHXKzjGw5G4n8lscf5s8QnE5O1P3NCH8Y123bLT6htIzLm3EzXgVhwleRyc/W9Cug4lrNvF7gy5VkWePYaynCRKM/YRc8F4gk87h8RvHqM4aSs+Tbqc+AZVENjhIor2LscoKyR7yTS8wlpSnr2/0hifuK54hbUkqHPFn2XOyg8p3r/Seb5oxyICO1RsFWv28iWw42BKDq3Dlp+BUV7kPF4VaT9PoTRlOxgOihLXA1B8oGJnpWnTpp1UknDkHqtXr8LcpBfm0FbHHuhd8ZzffvuNIUOGHHXIc889x8yZM3n22Wf5v//7v5OKS0SkJpQoiJziFi+u+FY57fcPaXrxQ7XSbKyhsAaEEX76JWSt/xHDcGCU5pOysGIbTu/QpsRd7NpPwWw2Y/YPpdUN09wdbq2z+gbR4urnKx0L7X4JxUlbKd63wvnB/WSYfQKJvmwq2UumU5a2/XCSYMLs7YfZL5Tg7lfhE1t5N6HQvjfhE9uR3DWf4RXWAv/4fi73jRj4CABFuxaTt+4LMn6dRpPhzx4zDkdZMclfTcRWnFtxwGRyrqiMiIjk7LPPZvz48Sf1XlNTU3nxPy9hDovHEtP1uGONokwA7Hb7Mcfs3r0bgLZt255UXCL1iYH7FxernlBzShRETnFHPoTk715J3o7fCe1U/xfl1qaIXleStf7Hit2AAFthJo7SQsqyDrDvs/tpdd3LHo7QvfyadMTk5Uvx/pW1kihARXIVcd49FO9fjVdUO6z+oSeOo2Uf/Foee+3HkQXl/m3PJX/jt5Rl7MZWlHPMe9tL8/9KEjARFh5JdmY6ALNnf8OAAQOq85aO6u67J5CRlYO5zbATjjWydxEdE8vjjz9+zDG7du0CoE2b4zelExGpKzXrLCQijcaNN97IJ598AkDKohkejsb9vIIiaXnV01gDwmly4V20v2MW7cd/hm90G8oy9+Nw2E58k0bCYSslY9mHGOUlmL1rf92FX8veVUoSqsNkMhHS+0YACnctOeY4r6BoYi97Fot/GGDw5hvTWbhwIUVFRbWSJHzxxRd89dWXOELaYjrBn51hK4Xc/QwdMviYU53y8/NJTU0FlChI4+IwPPOSmlGiICJcd11FZ17H4eZWULFbTUn6XopTdpK3awUF+9ZSkr6X7M3zyVj9NQ5b+bFu1+AEtOjOaWM/IKzrYEwWKxYffwJadAfDQerC6diKckie/wpZa78FIH/PSlJ/nYGtKM+zgdeygl3LyUv4DZPFm/Bz7vZ0OFXm2/R0API2fnfccSVJm7AXZXNmv34MGTKEgQMH4ufnd9LPt9vtXHvttQCYY7qdcLzj0Aq8LAaTJk065pg9e/YAEBkZSWho6EnHKCJSE5p6JCKsXr0agOB2/SjNOkjGHx+Tu2PZca/xj+uIf9PqNaFqSI5sE5u/YzH5OxYf/nkJGcs/dI4p2L2C+Fver1KDtQbBXPFXQtiA8Zi9T/4DtLsYthIATJajr68pzzlEec4hcv78jIsuuoh58+bV6j+zDz74oOL5QXGYLF7HHWvYSjFy9zP2rvF06NDhmOM07UhE6gMlCiKnuO3bt9O3b1+sAWHk7VhG3o5lWCwWHnroIXr16oW/vz9du3Zl1apVjBgxAoDgtn3xa9Lew5HXrYgel4HhoPDAegoPbMA3Kh5HeSllORXbyQa3H0BewlJyNvxE+BmXnuBuDYM1oKI7c/7mH/G54H4PR1N1JmvFjkeO0kLnsfLcZErTd+PXtBtJXz/gPP7uu+/WapJgt9uZPPlJTBYvLC3PO/EFjnIMh53WrVsfd5gWMkujZbi/M7NWM9ecEgWRU9yXX34JgK0w23ksLS2N8PDwSuOONKDyComl6SX/51xM2liZzBYie19FZO+rcJSXYrJYMZktGIYDDAPD4SBvx+9k/DGzwScKhsNO7rZFZCyfBYBvk84ejqh6TBYr3pFtKc8+gMPhwGw2k79tAflb5lQat2XLFpo3b16rz37ppZc4eDARc1wvTFafE443Siumq51zjmuPir9TRUFE6oNGUi8XkZqaNGmSc371jBkzMAzDJUkAKC0tBaA8N4XSjH3uDNHjzF4+zp4LJpMZk9mC2epVMc3EYWvwC56z139P2q8zcJQWYA1tSmDHwZ4Oqdp8m52OYS+jeO8KynIOUrj790rnN27cSKdONZ8ql5iYyK+//lrp2Pr163nkkUcw+0dgjqpacmUUphEQEEiXLsfvU3EkUVBFQRobB4ZHXlIzShRETnFeXl589tlnGIbBuHHjjjkuNDSUm2++GYCUxe+6Kbr6zTe6NWZvf8zmhluctRVkkbF8JgD+7S8k4tx7PRxRzRzZSjVr5UySv34QR8lfC81Hjx5N167H72twPCUlJdx33328Nm16peNLllTssmRuMxSTqYp/nRal0/307nh7H79fiaYeiUh9oERBRKrEZDLx/vvv061bN4oSN1KWm+rpkDzCMBzOaVrFKbuwBkZ4OKKTc+CrisZlZr9QQrpfidmn9rdFrWv2smJSf6joXOwozql0bvbs2Xz44YdHuarq7rvvPr7++mu+nf0NP//8s/P46tWrsQZGnXAB8xGGYWAqyeT888474djNmzezbt06unfvXtOwRUROWsP9GkxEPGL/gUQALD4BHo7E/RzlJez/6jGKU3Y6j/nGNtxF3YUH1mMryMArPJ7w8+/zdDg1Vrj9Z/jH9C8fHx/+/e9/c/nll5/0/c8//3zefPNNABISEhg8eDADBw7kl19+Ae8gqpYmAKV52MuK6dfPtdP0PwUFBXH66afXOGaR+srwwGJmty+ebkRUURCRarn++oqeC7kJx25u1ViU5SSR8tv/sBXnUZaTxPbpIyhO2YnJWjFtxDu8OdHn3enhKGsufck7AISfczdmS8P63qg8L5mcVbMo3LWY4t1LsVj/in/s2LEUFRXVSpIAEB8fz5gxY3jttde45557ALDZbJjNZkyOqvcTMUorOkO3b99wk0sRObU0rL8ZRMTjxt95J2/OmEHKb/8jvPvFng6nTtiKcsnb8Tspv74FQP7uFdiKcp3n2437HFtJPlbfo3fVbQgMw0FZThKWwKgG1TPhiPLsAxTv+4PifX8A8Nprr7F27VqsVisvv/wyd955JyaTiRkzZpz0Dl0JCQmER0QwYcIE57FffvmFuLimZJb5Vvk+juw9REVHaycjOaV5olOyOjPXnBIFEamWpk2bVvzgsFGafQifsKYVc68bwXapDls5SfNfJX/n7xgOBxaLhfPOO49FixYB4B3egqaX/RugQScJAMbhLtxmn4b5PnxjO5P7t99HjBjh/CBvGAbjx4+vlfn9Bw4cYOnS37npptGVju/cuZO0tFQsrQdV6T6GwwZ5Bxj76KRG8e+KiJwalCiISLXYbDaaxMWRnJTE7g/uxC+uE6VpuwloeQahnS8kqE0fT4dYI4bhIOGN6zDs5TRt2pSZM2dy/vnnYzKZmDRpElOnTsUaGImpkczYNFt9MHn5Yss95OlQaqRg2zwAWrZsydVXX+3s8wEVC+9raxGwj48PxcXFxMTEVDr+008/gcmEKSDqGFdWZhSmYTjsnHHGGbUSl0hDpTUKDYsSBRGplujoaJIOHWLWrFmMGTOG4qSthISEkLt7Bfm7V9B+/GdYfPw9HWaNHEkSduzYgb//X+9h0qRJvP76GxQcWMvemWOJGTgBe0kB5XmpeIc3I6TjQExmCw5bGWbr8be9rFcc9opvuhsQe0keZqsvpanbCQ0LZ+/evXX6DX1MTAwzZ7rumpSYmFjReC/vIKaw43dZBnBkbKd5ixYMHtzwelSIyKlLiYKI1MioUaMYNWoUAPn5+QQHBwNQdHATQW36ejK0ajMcduzFFfvue/v4VkoSoKKHRFpaKgkJCVx99TXsXvDfSufzti7C4hdC4b7VWAPCiL3ofvybHb+hlqcZDjuGvRyTtepz7N3JlpeKo7wI74h4oKLiU3xgNfl/foxhGBgOO70uvNBj03iefvppDhw4wHc//IQR2uq4fRQchakYuQd44MlXCAxseNvPisipS4mCiJy0L774wvmzd3izOn+eYbdRcGA9Ac27Yrb6uJwvStpO7rZfieh1BbbCbBxlRQS26glAeV46qUvepShxA00G3YthGBz8YQpQMWXltVf/e9Rn+vn5cfrpp7N27RoefPBB+vXrx8iRI7nlllv55JOPneNshdkc/PbfBHccSOzAu2v/zdcCwzBI/eV1AILPuKZO7n+8D/C2/DRyV3+ExS+Y4J4jKd63gsKEBRgOO+HnTMBWkE7OH/8DwOztjzW0GWVpOwAYNuwSzj67PwEBAYwZM6bWY6+qoKAgHnzwQWbPno2pMA1TYOxRxxmGA8fu+bSKb8WddzbcHbJEaosnOiWrM3PNmQxDM7dEpOZ27Njh3O6x/Z2fYPGt+29MkxfNIHvjXAAiz7yOyN7XYLZW7GZvGAZ7Z91NSWYiJqs3hq0MgLjB9xHS8Vz2f3o/poI0CgsLKt2zR48ePPLII4wYMaJasdhsNjZs2EB8fDzh4eFs27aNTp06Yfbype0dn9bCu61dGX/MImvNNwD4xHUl/Oza/fCa/+csig+uJ+yCB/EKbuJyPuv3GZQmbarSvW6//XbeeecdvH18uObqqzn99NOZOHEiVmv9+I7L4XDQvEULkot8sLY81+W8YThwJK3Bkb6Z+fPnc84557Bp0yZ69uypBc1yysnLyyMkJITlCQcIDAp267ML8vM4q30LcnNzndVvqZr68V9bEWmwoqOjAQjrNtQtSUJ5frozSQgICCBjxWdkrPiMltc8h3/TziT9NIWSzEROP/101q9fD0DfvmeyasFrYNgpSt3Du+++i7+/PxPuuYerr7qK1157DS+vKrfNqsRqtdKzZ0W1Ytu2bc5vuaPPH3/yb7aWOMpLyd+xhNytCylJrfhm3r/9hYR0v7JWn2PLT6NgT8V2pUW7lhDS41ocZcVgsVK8ZxmlaTuOmiRERESQkZHBd9995+x9YDabeeWVVzjzzDO54YYb8PFxrRx5mtlspmePHvzwww8YLc5x+fDvSFkHmdt4/PHHufDCC3nqqaf4bfFSXnv1Fbp27eqhqEU8S4uZGxYlCiJyUkJDQ+narRt7sg/W6n0dZcXk7vgdn4gW+DepqFgYhsHeTx4AYOPGjXTt2pW5c+dy3XXXsf/L/6PJRRPI3bmC66+/nnfffZeBF15Ek9hYPv/8MwYNHsKv818D4KyzzqJDhw5cd911tRrzY489xsqVK7F4+xN82oBavffJSJo7laID6wCwBEQS1v8OvEKb1vpz8jbOdv5ctGsxRbuWwDFK/k2bNSPp0CGuvmYEn336CQDDhw/HZrORkpKCj48PAQEB3HLLLbUeZ2267LLL+OGHH3CkrMfS5K8djQyHHVPWDkbfNJonn3wSgFtvvZXIyEg6derkqXBFRKpFU49E5KRs2rSJbt26EdLhXJoOfaDW7pv4/bPk714JgE9YHLbCLOxlJQA899xzPProo86xy5cvp3///gDEt27Nst9/p0mTytNe8vPzueqqqykuLWXh/J/r5Btqk8mEV2gcLa5+Hks96bNQlpPMvo8qqhtRlzyL1T+szp6VvmAqtuwDjB17O2+//fYxx5nNZg4cOPBXT44GzDAMRo4cyWeffYYprC2Wpn0wWX1w5B/Cvns+a9eu1ZaoIvw19WjZds9MPerfQVOPaqJxbAguIh5z7eFv5aMH1O7C0vzdK/H29sbL25vS7CQuPP9cQsPC6NGjp8ui0LPOOovNmzfz+OOPs2D+fJckASoWn86f/zNLF/9Wp9NYynOScJSX1Nn9q8srONr5c10mCQC27P1g8SI1Ne0o6wgqpuU0b96ct99+u1EkCVCRHH7yySe8/vrr+JenwL75GA4bjuy9NG/egtNPP93TIYrUKw7D8MhLakaJgojUWEZGBtu2biWi5xV4BYbX2n2Tfv4vAFOmTCE5KYns7GzmzZtHVmYma9b8SWhoqMs1nTt35sknn6RNmza1Fkd1/fe//wUge913HosBKhbRFuxdTUnabnK3LnIed9hrr2eC4XBQkrzFuVjc4XBUnLCXMXnyE9hs/3yWQYsWLUhISODWW2+ttTjqA5PJxPjx4/n1l1+wFWTgyNqFtTyPc891XbcgItKQKFEQkRobNGgQACarF5lrvyNpwXTydi7nZGc0luWmAHDPPfcQERHhTAzq+4eue++9F4D8Xcs9Gkfu5vkk/fQcB754kLTfZgAQ2PkSzJbaW5aWv+Ebspe+TtGeZQDYshMBMzExMVxyySXOcX+v3jz88MP4+/szZcqUWoujPunduzcXDByIOW8fYGA2u/4Va7PZ/kqqRE5BdodnXlIzWswsIjX2r3/9i6uuuoqMlX/1UcjZPN/5c2CLrjQb/vhRex0cj1+TDhQd2kppaWm92Qqzqnr26sWaP//EVpSD1T/UIzGYvQ83UbN4YfYOwCemA0GdL67VZxTtXgxAceKflBxchy0/GXDQokUL2rVrx7I//qBzx050696N5w8nBp06dSIkJIRzzjmnVmOpT6IiI7HlJWPxCXRJFDIzM4mMjKRNm7bs2rXTQxGKiFSdKgoiUmNXXnklJSUlbNy4kZSUFDZv3sxdd91Fr969ASg4sInCxM3Vvu+RrsZHOj83JBkZGQDseW8Me2eNJy9hcbXvYdjLMezl1b/OMLAVZuE4vOg7oN0FxFz6HKF9Rlf7Xke9v62MvPVfkb/5R2fyV565l7KMXThKC3n22WcJDg7mm29mExEWxg03jmL9unVYvSrGNmnShJycHOfC88boSOdle2kB4eGVp+MFBVUscN+9exelpaVuj02kPtAahYalYX1VJyL1jo+Pj3NP+JiYGKZPnw7AN998w1VXXYVXQPUX0Po36YB3WByzZ89mwYIFXHTRRbUac116ffp0VqxYQXJyMu+++y4pC/5LcHvXZlzHUpqVyP5P7sHLP4SWN87A7O1XpetSf3md/F3LKvoWHGYvTK92/M5rS/IxmS2YLF6UZe7FOyKezIVTKc9LrhhgshAaFkZOdg5gsG/fPlq2bEl2dg7Jyak89dRTXH/99eTn5/Piiy9yzz334O3tXeN4GorLL7+cd999F4BLL7200jlvb2/Ky8vJysqql30hRET+SYmCiNSJvXv3YjJbMPsEYNjLMVmO3tCsJGM/SXNeJLLfSILbnQWAxTeQ+Ov+Q8KMkXz55ZcNKlEYNmwYw4YNAyBhx05+X7qEg98/RUCLMwjpfBFmL9/jXl+avgeA8qJcygsyODj7X5isPrS6/tVKSUNJ2m6yN/xA9Dm3g2GQu3UhJqsP3lGngcWKxTeY4G5Vb6hWtG8lpUkbCew8jIyfnzl81IR/6/4U7fkdTBYw7Pj4+BAWFk6HDh3w8q74Z3rvPffQsmVLAF588QVefPEFACZOnIjJZOLBBx+schwN3SWXXILdbic5OfmoOztZrVZnk0IRkfpOfRREpE7s3r2btm3bAhDUsjvNr3y60nlbUS6Hvn+awuSKTsF+se2Iv/4l53nDMNg543pOa9OKLVu2uC/wWlRcXMw777zjXOSMyUzkWaMJatufgt1/UJp1gIjeI/AKiqI8L439nz+Ao7TgqPdqNWoG3iGxAJQXZLD3g9sBCOtxBfkJi7EVZhHadwx+LXvXKNacVTMp3rfC+XtcXBxJSUmH4za5tDbNy8tzTqWR6svKyuK6667jxRdfpHv37p4OR6TOHemjsHDTXgLc3EehMD+PC7vGq49CDWiNgojUiTZt2vDee+8BUJSyi9LsJLI3z8dRVoyjvISDPzyHqSCFDz74gKFDh1KcspPilMoLPG2lRWzdupXc3FxPvIWT5ufnxz333MPOnTsZMGAAGA4yln3A3g9vJ/3398jbupC9H47l4PdPsXfmHcdMEgD2zbqTQ988ir20EJPpr/90Z6+dja0wC5N3AD7Ne54wJsP4a/uP8uxE0uY8QcG2nyslCQAff/wxe/fu5ffff6d3r14u91GSUHOGYXDLLbewYMECbrzxRu2CJCL1lioKIlKnAoOCKCz46wOwNSAcs9mMvSibr7/+muHDh/Pdd99x+eWXE9HzCmLOqWjcZthtbHvtSkJCQsjJyfFQ9LXn4MGDrFy5kuTkZHx9fTnrrLNITU1lyNChlP1jYWuz5s3ZkZBAZmYmUNF5+tprr604aTJjDQjDVpBZ6ZrAjkMJ7HwxJrMFqOhzYPrHrjuO0kJSv3vomDGOGDECHx8f7r33Xnr2/CvpcDgc/Prrr7Rp04YHHniAf/3rX+o2fBJeffVVJk6ciLe3N3/88Qc9evTwdEgide5IRWH+xr0EuPmLhsL8fAZ1U0WhJrRGQUTq1KqVK7n55pvp0aMHkZGRfPzJp5zWrh2PPfZ/zm0yt2/fDoBPZEvndY7DjbyeeeYZ15s2QM2aNaNZs2aVjnXq1ImiwkLKy8vZunUroaGhtG7dutI1AAMGDMDLy4vy8nIwHNgKMjn9jDO47tprWbBwIYsWLqRg21x8mnbDO7wlhmGQ8tXd+DTpQviA8RTu+IXCrT9h/9tC5yNef/114uLiOPfccwkLO/rCc7PZzMCBAwH4+uuva+uP5JT0559/8tBDFcnaSy+9pCRBROo1JQoiUqc6derEqlWrnL8f7YP/RRddxKRJkyjcvQK/mHYUJW/H4hMAwNy58xg/fvxRm1c1BhaLBYvFctwPjE2aNOHQoUMsXLiQ7t2706lTJ+e5xMREFi1ciMU3CK/gJsBfjelKkzdTtG8Feeu/4sILL6RHjx6MGzeO+Ph49u3bR3R0NP7+/nX7BsUpNzeXESNGUF5ezpVXXsldd93l6ZBERI5LU49EpF7o2rUbmzdvOuq5DRs20K1bNzdHVP+VlZXRp09fEg6kE3bRY5XOJX8xvtLvNpsNi8XizvBqjcPhwGQy1fvO3MdjGAYjRozgq6++olWrVqxbt87ZcVzkVHBk6tHc9Z6ZejT0dE09qonG+RWdiDQ4n332KTfeeCMzZ87kp59+olWrVgB06NCBLl26eDa4emDt2rUu036+/PJLNmxYj+9pf20f6ygv+StJOLzo+dlnn22wSUJCQgLBISE0iWvKtm3bPB1Ojc2YMYOvvvoKq9XK559/riRBRBoETT0SkXqhc+fOzJo1y/n73r17ycjIICIiokF/k1xbHnjgQdIzMrnqqqucx/z8KvoqWEP+2q8/dfb9f110eIejcePGuSfIOpCcnExhQQGFBQXMnz+fjh07ejqkalu/fj3331/xz2Xq1Kn06dPHwxGJeI4nOiWrM3PNqaIgIvVWZGSkkoTD3nnnbWa8Mb3SsU2bNuHlG4g1pMlxr50wYUJdhlanjvRyaN6iBSNHjvRwNNWXn5/PiBEjKC0t5dJLL+W+++7zdEgiIlWmioKISAPQtm1bZwO7I1q1akV5SQGF2xcQ2HEwAH7Ne1KcuAaA22+/nXfeecfZKbohGjFiBGvWrOGMM84gKirK5bzdbufXX3+lRYsWnHbaaR6I8PhKS0tp1aoVJSUlvP/++0p85ZRnNwzsbv6G393Pa0y0mFlEpIGy2Wx4eXnhFRJH5OB/ARX9Jwq2zaVg61yCQ0LIzspqtDtGrVmzhl5/awa3devWejk1yeFwkJiYSMuWLU88WKSROrKY+fu1uz2ymPmyHm20mLkGGuffHiIipwCr1UqzZs0pz01ydnU2WawYACYzebn5/PDDDx6NsS5t2bKl0u/r16/3TCAnYDablSSISIOkREFEpAF7+eWXAMj+413nseAul2IOiAQcvPPOOx6KrO6NHj2aF1980fn7oEGDPBiNiFSFA3AYbn55+k03YEoUREQasCuuuAKAsrQECnctcR6PHvoE1sAoSsvKPBWaW9x3331s2LCBwsJCIiIiPB2OiEijokRBRKQBs1qtjB49GoCSxDXYCjOx5aeSu/YLbAXp9Ond28MR1i2LxUK3bt3UYVqkgbA7DI+8pGaUKIiINHBXXnklAGXpO0n/6d+kz32S4t2L8fLy4pNPPqG0tNTDEYqISEOkREFEpIH79ddfgYrtUNu0aQNUdLRu0aIFcXFxDBo8mF27dnkyRBERaYDUR0FEpAEyDIOlS5eSkJDg7B8QEhLCgcREALZv3w7A7t27Abj22usYO/Z2LrnkEpo2bUpaWhoRERFYLBbPvAEROSUZHujMrE4ANac+CiIiDYRhGLz00ktERESQmJjIE088cdRxJi9ffOO6U7x/pfOY1TcQW0kBN998M0899RSPPPII06dPJzw83F3hi8gp7Egfha9W78Q/0L19FIoK8rm6dzv1UagBVRRERBqIO++8k7feeqvSsYiBD5O7eiZYvLAGRBHU/QqsARW7/wSffjWp3z1EUPcrCWh3PrlrPmXBwkU88cQT3Hjjjfj4+HjibYjIKcxuVLzc/UypGVUUREQagMTERFq0aAGAf5sBlCZvwb/tuQR2uKjK9yjav5LclR9SUFBAQEBAXYUqIuLiSEXh81WeqShc20cVhZpQRUFEpJ4zDMO55gAg4LSBhPS8vlr3KE5cS1nqDgDWrVvH2WefXasxiohUhcMDaxTc/bzGRLseiYjUY4ZhMG7cOAYNGoR3SAzRlz6HNSi6WvdwlJeQ88f/KN73B8EhIcTGxp50TB9++CFbtmw5qfuIiNR3U6ZMwWQyMXHiROcxwzCYPHkycXFx+Pn5cd5557n897C0tJQJEyYQGRlJQEAAl112GQcPHqw0Jjs7m1GjRhESEkJISAijRo0iJyfHDe+q6pQoiIjUU9nZ2Vx99dW8/fbbBJ9+NeEX/QuLX2i171OWtsP586GDB2nbtu1JxZWQkMD0N9/lkUmTTuo+IiL12erVq3n77bfp1q1bpeMvvPACL7/8MtOnT2f16tXExsZy0UUXkZ+f7xwzceJEZs+ezWeffcbvv/9OQUEBl1xyCXa73Tlm5MiRrF+/nnnz5jFv3jzWr1/PqFGj3Pb+qkJTj0RE6qG33nqLe++diM0wEXrmLfi16FWj+5Sl7yJ72Zuc1r4DmzdtxMvL66Rja9euHbeMvl7Tl0Sk2jzRKbkmzysoKOCGG27gnXfe4ZlnnnEeNwyD//73vzz22GPOZpcffvghMTExfPLJJ9xxxx3k5uby7rvvMmvWLC688EIAPvroI5o3b87ChQsZPHgw27ZtY968eaxYsYK+ffsC8M4779CvXz8SEhJo3759Lbzzk6eKgohIPTRu3DhKS0sIv+ChGicJAIW7fgNg/s/zaiVJALBYLNx555107dr1qOdTUlJ49913STzc00FEpD7Iy8ur9Dpe1/q77rqLYcOGOT/oH7F3715SUlIYNGiQ85iPjw/nnnsuy5cvB2DNmjWUl5dXGhMXF0eXLl2cY/744w9CQkKcSQLAmWeeSUhIiHNMfaBEQUSknrL4h2MNrvl6AkdZEaVJmxgzZgwtW7asxciO7+67J3Dbbbdx7XXVW3AtIo3fkcXM7n4BNG/e3LkeICQkhClTphw1xs8++4y1a9ce9XxKSgoAMTExlY7HxMQ4z6WkpODt7U1YWNhxx0RHu643i46Odo6pDzT1SESknsnKygLAK6z5Sd2nJGkjhr2c0aNH10ZYVXbLLWPYs3cv4+8c59bniogcT2JiYqXtUY/WSyYxMZF7772X+fPn4+vre8x7mUymSr8bhuFy7J/+OeZo46tyH3dSoiAiUs8EBwfj7x+AOSTupO5TlrIVLy8vevToUUuRVc3FF1/MxRdf7NZnutMff/xBbm4ugwcPrld/oYvI8QUHB5+wj8KaNWtIS0ujZ8+ezmN2u50lS5Ywffp0EhISgIqKQJMmTZxj0tLSnFWG2NhYysrKyM7OrlRVSEtL46yzznKOSU1NdXl+enq6S7XCkzT1SESknrFarURFx2Avzq3xPRzlxRQf+LNKfzFK1TkcDu6++26GDh3KtGnTPB2OSINzpDOzu19VNXDgQDZt2sT69eudr169enHDDTewfv16WrduTWxsLAsWLHBeU1ZWxuLFi51JQM+ePfHy8qo0Jjk5mc2bNzvH9OvXj9zcXFatWuUcs3LlSnJzc51j6gNVFERE6pmSkhIOHUrEEulX43sU7VoMQGZmZm2FJVTsXLJ27VqCg4O5/nqtwRBpbIKCgujSpUulYwEBAURERDiPT5w4keeee4527drRrl07nnvuOfz9/Rk5ciQAISEh3HrrrTzwwANEREQQHh7Ogw8+SNeuXZ2Lozt27MiQIUO4/fbbeeuttwAYO3Ysl1xySb3Z8QiUKIiI1Du+vr48cP/9/OeVV6s9X9VwOCjcsZD8Td8D8PDDD9dVmKec8vJyJk+eDMCjjz5KVFSUZwMSaYAaQ2fmhx9+mOLiYsaPH092djZ9+/Zl/vz5BAUFOce88sorWK1WRowYQXFxMQMHDuSDDz7AYrE4x3z88cfcc889zt2RLrvsMqZPn16rsZ4sk2Gor7WISH3zzjvvMHbsWGKvehWTpWrbmhoOOznL36YkaRNQeYcNOXlH/plER0ezZ88eAgICPB2SSIORl5dHSEgI7/6+Df/AoBNfUIuKCvK59eyO5ObmaipmNWmNgohIPbRt2zYACnctxnDYTzAaDMNB3rovKEnaxGeffYZhGEoSalFpaSlPP/00UFFNUJIgUjMOh+GRl9SMph6JiNRDY8eOZXtCAvPmzqZ0z1K8m/XCr1VfrEGV990uTdlG/qbvKM8+AFQskLv22ms9EXKj9sEHH5CYmEhcXBzjxmnbVxE5NaiiICJSD3Xo0IE5P/3EokWLaNsihoJtc0mfO5msJdPJWTWToj3LMew2spZMozz7AL179wbg5Zdf9nDkjY/D4XD+uT7yyCPH3VtdRKQxUUVBRKQeO//881m75k8SExO54cZR+Ph4s3bNWnL3rSD3z48AOPvss1m6dKmHI228Fi9ezI4dOwgODuaWW27xdDgiDZqjmtuV1tYzpWaUKIiI1HM+Pj60bduWlSv+AGDfvn3Ex8cDMGDAAN5++21PhtfozZw5E4ARI0YQGBjo4WhERNxHiYKISAPTqlUrDMOo9tapUn2GYbB161YAbr75Zs8GI9IINIbtUU8lShRERBooJQl1z2QysWLFCtauXUuPHj08HY6IiFspURARETkOk8lEz549PR2GiIjbKVEQEREREbewGwZ2N08FcvfzGhNtjyoiIiIiIi5UURARERERt/BEp2R1Zq45VRRERERERMSFEgUREREREXGhqUciIiIi4hZ23N+Z2e7exzUqqiiIiJzCNm7cyJIlSzwdhoiI1EOqKIiInKIOHDhA9+7dAUhNTSU6OtrDEYlIY6fOzA2LKgoiIqeouLg4AIYPH05UVFSlc4mJiUyYMIEhQ4Ywf/58T4QnIiIepoqCiMgpymq1Yhzjm7agoCDCwsJY8eefbNiwgUGDBjnPLV26lO7duxMcHOyuUEWkkVDDtYZFFQUREXGxe/dufpwzj9zMTDZv3uw8XlBQwJKly1i2bJkHoxMREXdQRUFERFzEx8dz9lln8vSTTzBs2DAAtm3bRlZWNreMuYnY2FgPRygiInVNiYKIyCkuOzubLVu2EB0dzWmnnQZAeHg4r732GgCGYfDnn38SFRVFTu5emjRp4slwRaQBczgM7OrM3GBo6pGIyCksKyuL0TfdxFtvvcXOnTuPOmbixIn06dOHVatWcfHQoW6OUEREPEWJgojIKSw3N5cdO3eRlJxMUFAQRUVF3H///Xz11VfOMUOGDGHMmDG8+tp0pk2b5sFoK7Pb7Tz++OMsX77c06GISBXZD1cU3P2SmjEZx9ryQkREGj3DMEhLS+P6669n//79jB49msmTJ2OxWCgqKuLHH3/kqquuAsDL24egwAAyMzM9HHWFDRs2cOkVV9O35+l8+eWXng5HRI4jLy+PkJAQnp27Dt+AILc+u6Qwn8eGnkFubq52a6smVRRERBqQ4uJi3nrrLex2e63cz263Exsby6+//sqePXtYv349P/zwA7t378bb25uCggIA+vfvz8wPP2DdunUn/cx///vfmEwmTCYTxcXFNb5P165duXvc7Tz77LMnHZOIiLjSYmYRkQbC4XDQt29fNm3aRG5uLg8//PBJ3/NIwmGxWnHY7bz55pvExMQ4z48ePZoOHTrw5Zdf0qxZM1q0aFHtZ+Tl5fHTTz/x9ttvk5aWxqFDhyqeabEwd+5crrzyyhrFbjaba+XPQETcxxNTgTT1qOaUKIiINBBms5mJEydy66238vjjjzN48GC6d+9+Uvf08fHBMAzy8/OxWq0UFRVVOp+VlUXfvn0B8Pb25uyzz67yvUtLS3nggQf4+ONPyMvLx2E4MJtNjB41ipWr/+RgYiJXXXUVmZmZhIeHn9T7qE1z5syhuLiYCy64gLCwME+HIyLiMUoUREQakDFjxjB79mx+/PFHrrvuOtavX4+Pj89J3/fIQuaIiIhKx8PCwpg+fTrBwcGMHDmyyvez2WyMHj2atRs20a5dW4YMGUJERASzZn3M4iVL8PXxoXXr1nTt0pnQ0NCTjr82jR07lkOHDrFmzRolCiK1zO5w/zf8dodbH9eoaI2CiEgDYjKZ+OCDD4iJiWH79u289NJL1b5HZmYm27Ztcznu7+9/1OfdddddjBo1CovFUuVn3H777fw0Zy43j7qBFStW8OSTT7J7z16Skg5SVlZOSkoqBQWFvPjii5jN9euvoiN7fJhMJg9HIiLiWfXrv84iInJCERER/Oc//wFg6tSpZGdnV+v6pKQkcvPyOd6md2VlZfzfv/7NjBkzahRjaGgoFw+9mMceewyz2cz019/AYjZx/fUjuWXMzfTs1Yt7JtylDs8ipxhtj9qwKFEQEWmARo4cSZcuXcjLy+Odd96p1rVdu3blzL59jvuNuclkoqy0hF69elXr3nPmzOG660fi4+PLxcMudh5v17YNxcXF+Pv7sX7jZk4/4wzuueeeat3bXVRREBGpoERBRKQBMpvN3HfffQC8/fbbx60O1ISXlxf/efFFevfuXe24Nm7cxO7du+jdqycAmzZtYu269ezZs4/fFi/htLatmfTwQ7Uar4iI1D4tZhYRaaCuvfZaJkyYwO7du1m3bh09evTwdEgMGTKE7dsTmDp1KsOGXUKv3r3Izs6hebOm/Pjj9zgcDnx9fT0d5nGpoiBSd7Q9asOiioKISAMVEBDAwIEDAZg/f76Ho/nLxIn3snTpEs4771yaxsXRrGkcN9xwA97e3vU+SRARkb+ooiAi0oBddNFF/PDDD/z2229MmjTJ0+E4tW3blg8++MDTYQDQvHlzfH192bFjR5WqBKooiNQdhwcqCg5VFGpMiYKISAPWt29fOnbsWO21BKeSgwcPAhXrJ2p7LYeISGOmREFEpAHr06cPW7du9XQYDUJwcHCVxqmiICJSQYmCiIg0aocOHSI5ObnKi71nzZpFWVkZLVu2rOPIRE49dsMDi5lVSawxJQoiItKoxcXFERcXV+XxgwYNqsNoREQaDiUKIiIiIuIW2h61YdH2qCIiIiIi4kIVBRERERFxC1UUGhZVFERERERExIUSBRERERERcaGpRyIiIiLiFjaHgcXNU4FsmnpUY6ooiIiIiIiIC1UURERERMQttJi5YVFFQURE6oXU1FRuuukmli5d6ulQREQEJQoiIlJPrFixgjUbtvDNN994OhQREUFTj0REpJ4YPnw4bdq0oW3btp4ORUTqiMMDU48cmnpUY0oURESk3ujSpYunQxARkcOUKIiIiIiIW9gNA7vh5sXMbn5eY6I1CiIiIiIi4kIVBRERERFxC22P2rCooiAiIiIiIi6UKIiIiIiIiAtNPRIRERERt9DUo4ZFFQUREREREXGhioKIiIiIuIUqCg2LKgoiIiIiIuJCiYKIiIiIiLjQ1CMRERERcQu74cDucLj9mVIzqiiIiIiIiIgLVRRERERExC0cHljM7NBi5hpTRUFERERERFyooiAiIiIibmF3GJi1PWqDoYqCiEgj069fP+Lj41m3bp2nQxERkQZMiYKISCOTmZnJvn37KCws9HQoIiLSgGnqkYhII1NSUgKAt7e3hyNxj8TERGJiYmrl/a5YsYLIyEjatGmDyWSqhehE5O9sDjC5eSqQTbuj1pgqCiIijYhhGKSnpwMQFRXl4Wjqnt1uZ8GChaxatapW7nfbbbfRrl07fvzxx1q5n4hIQ6aKgohII5KSkkJJSQkmk4m4uDhPh1PnLBYLF1xwPs2aNTvpe6Wnp7NlyxYA+vbte9L3ExFXWszcsChREBFpRDZs2ABAu3bt8PHx8XA07tGqVatauc+CBQsA6Nq1K9HR0bVyTxGRhkxTj0REGpFff/0VqN/fiGdkZGAY9e8bvm+//RaAiy++2LOBiIjUE6ooiIg0Ena7nU8//RSofx929+zZw/fff8+ZZ55Jv379iI6JYe+ePfj7+3s6NAAKCwud6xKuvvpqD0cj0nhp6lHDokRBRKSR+Pzzz0lMTCQiIoLhw4d7OhynoqIiLh42jOTkZB6dNAmAkpIy9u/fT8eOHT0cXYU5c+ZQXFxMfHw8PXv29HQ4IiL1ghIFEZFGwGazMXnyZAAmTpyIn5+fZwP6G4fDQZMmTRhw9tn88suvhIRHccVlw+jQoYOnQ3N66623ALjuuuu0LapIHVJFoWFRoiAi0ghYLBbeeustXn31Ve69915Ph1NJYGAgv/7yCwBLlizh22+/5aqrrmLTpk1069at1p9XUlJCWVkZwcHBVRq/Y8cOFi1ahMlk4o477qj1eEREGiolCiIijYDJZOL888/n/PPP93QoLqZPn86iRYto3bo1L730Eu3ataNP336YTQYJCQlkZmbStGnTWnmWw+GgQ8dOZGZmcMcdd9KyRTNCQ0MZMWLEMXeBmjZtGgDDhg2jZcuWtRKHiBydw2G4/Rt+hyoKNaZdj0REpM4sWbKEp599lu+++56lS38H4JFHHiHpUCKl5Xa2bNmCzWar8f0djsotV00mE5mZGRTk55Oekc769evZuTeR33777ajXp6en89577wFwzz331DgOEZHGSImCiIjUie3bt/Pc8y8y8PzzWbhwAb//vpTPPvuM5X/8QevWbZj26it8++23tGjR4pj3OHDgAFOmTGH//v3OY7t27WLDhg0UFxczbdr0Sl2ZTSYTP8+bxyeffMKH77/H66+/zll9etC+fXsyMzNd7r9s2TJsNhs9e/bkwgsvrN0/ABGRBk5Tj0REpE4EBQXRrk0r7rnnHtq1a8err77KxIkTgYp1BH379mXDhg08/vjjeHl5uVyfm5vLk089w/4DBwA477zzOOOMM0hKSiIpKZl27dpx6NAhVq5cic1m56yz+lFeXk67du2w2+2kpqYSExNDz549iYmJ4cwz+7F8+bJKz7j88svZvXs3WVlZWsQs4gZ2h4FJi5kbDCUKIiJSJ5o2beqc/+/r60tpaanznI+PD59++ine3t5HTRKgojrQs8fp3H7bLRQVFTNr1ix2797NjTfe6Bxz113jSU5OpqSkBICHHnqI35b+wYa1q/Dy8qKwsJCvv/4awzDw8T36GoVmzZrRrFmz2nrbIiKNhhIFERGpc23atGHr1m0ATJxYsSvTiXooBAQEMHr0aAICAigoKGDgwAuIj4+vNMbHx4fevXuzZ88e3nzzTQ4dOsSBvbsAKC8v54cffmDD5u2MGzeO//znP3XwzkSkOgzDwHDzN/z1sRN8Q6FEQURE6tyWLVvYv38/ERERBAYGVjo3Z84cFixcyCsvv+w8lpmZyeAhQ+jTuw+PPjqJN998k2HDhjFw4MBK1w0bNoyhQ4eSnp5Bdm4u/fr2oe1p7fnw/XcpKiqibdu2pKamcuONNxIQEOC29ysi0hgoURARkTpns9kIDAxk48aNbNu2jauuuorQ0FAA2rdvT3FxcaXxCQkJ7Nq1i6zMTEJCgnn++ee5+uqr6dGjBwCPPvoon332GQA7duykRavWPPXUkwwdOpT8/Hyys7OdFYs777zTfW9URKQRUaIgIiJ1yjAMHnvsMUJCQpg16yN27d7FbbfdRlZWFmFhYSQk7KBTp06VrjnrrLP44P33CQwMpEWLFmzZupVrr70WgKysLJ5//nkALBYrS5cuITg42FkxePHFF1m9biOfffQhISEh7n2zInJcDofh9r4G6qNQc0oURESkVmRnZ/PRRx9xzTXXEBsb6zx+8803M3PmTEJCQigoKMRur+ibcKSikJ2dRceOHZg0aRI+Pr48+ugkfH19ufzyy533+P6775w/BwcHEx4eQVZWJhMm3E2TJk0qxdGrVy9SUlJcpjiJiEj1qI+CiIjUCpvNxoYNG7Db7bzwwgvk5uYCsHHTJlq0aInJbCYsLJSxY8dSVlbm3I70hhtuwMfHh6lTp7J69aqjdlDeuXMn/3npZfbt24fVaiUzMwPDMHjllVdcxl522WVEx8TyySef1O0bFpFqMwzDIy+pGVUURESkVkRFRfG///2PH374gUceeQSz2cz48eP54fvvWblyJYsWLeK8887D39+flJQUmjdv7rw2NTWV3377jd69ex+1n4HVaiU9Pb3KXZxT0jIAfTgQETkZShRERKRWHVkrEBoayrp16+jfvz9FRUU8+ezzvP/++5SUlNCkSROSkpIAKCoqYtWqVbRu3Ro/Pz+mTp1K27Ztueqqq5z3jI+PZ+rzU6ocw9szpquBmojISVKiICIiteqCCy4gKSkJX19f/Pz82LFjB3fcMY7i/BzKym0EBQXxzTffOMf7+/tzySWXEBsbS05ODk8/8yw+3t5ceeWVFBQUYLVa8fPzc44vKCjgtddeIzAoiP5nnUW3bt1cmraZzZpZK1IfGQ4P9FHQYuYaU6IgIiK17u8LjFeuXMmu3bsxmaBN69bMmPEGO3fupE+fPs4P9E2bNgUgLCyMr778gqioKEwmEx06dqJly5YsX/a7837/+c9/mD7jbXy8zAwdMoRevXoxbtw4975BEZFTgBIFERGplr1793LjjTfSsWNH/ve//51wfGFhIfHxrQkKDuasfn3Jyclhz74DZGZmEhUV5TJ+yJAhzp+TDh0k6dBBABITEwkICGDMmDEsWbKEiIgo+vTpQ5cuXWrvzYlIndL2qA2LEgUREakym83G2QMGUFZu48wzz6zSNbfddhv9+vUjKioKHx8fwsPDOffcTCIjI0947cMPP8yIESMAmPb6G4SHhjB+/Hjeeust2rVrd1LvRUREjk+TOEVE5JjmzZvHAw8+SGlpKQCHDh0i6dAhLh12MS+99FKV7mGxWOjevTtxcXFERERgMpmOmSSUl5dTUFDg/H3q1Kn07NkTgFE3jOS6665j7ty5zJ79rXOMzWbj888/Jzk5uYbvUkTcxXB45iU1o0RBROQUtn//fjZv3szom27i559/djlfXFxMdm4+JSUlALRs2RLDMHjvvfeqdP/S0lJuue0O3nrrrSqNHzPmFs7qf7bz95KSEj755BNKSkro2rUrrVq14rLLLmPkyOsrPSMtPb1SgiEiUhNTpkyhd+/eBAUFER0dzeWXX05CQkKlMYZhMHnyZOLi4vDz8+O8885jy5YtlcaUlpYyYcIEIiMjCQgI4LLLLuPgwYOVxmRnZzNq1ChCQkIICQlh1KhR5OTk1PVbrBYlCiIipyiHw8EVV17NuHF3snz5H6SlpbmMueKKK3jvnbcICQmp0j1Xr17tTCqgoprQqeNptGzZskrXx8e34oweZzh/nzlzJjfccANffvml85ifnx/NmjVz/h4QEMCEu+/WVCQROWmLFy/mrrvuYsWKFSxYsACbzcagQYMoLCx0jnnhhRd4+eWXmT59OqtXryY2NpaLLrqI/Px855iJEycye/ZsPvvsM37//XcKCgq45JJLsNvtzjEjR45k/fr1zJs3j3nz5rF+/XpGjRrl1vd7IiZD7epERE5Z06ZNo0+fPrRu3fqoC4uro6ioiGnTXyeuSWyt/WVnt9vZuHEj3bt3d+6QtGHDBvbs2cPFF1981C7OIlL/5OXlERISQp8nvsfqG+DWZ9tKCln15GXk5uYSHBxcrWvT09OJjo5m8eLFnHPOORiGQVxcHBMnTuSRRx4BKqoHMTExTJ06lTvuuIPc3FyioqKYNWsW1157LQBJSUk0b96cOXPmMHjwYLZt20anTp1YsWIFffv2BWDFihX069eP7du30759+9r9Q6ghVRRERE5hEyZMoG/fviedJEBFP4TLLr2Eyy67rBYiq2CxWDjjjDMq9UWw2+2kp6ezZs0aFi9eXGvPEpHGLS8vr9LryNqr48nNzQUgPDwcqNj1LSUlhUGDBjnH+Pj4cO6557J8+XIA1qxZQ3l5eaUxcXFxdOnSxTnmjz/+ICQkxJkkAJx55pmEhIQ4x9QHShREROSY0tPTK5XKDcPgWIVou93O9SNvoN1pp5GXl1cn8TgcDnr06MGoUaP46OOPef+DD7HZbHXyLBGpfUe2R3X3C6B58+bO9QAhISFMmXL8bu+GYXD//fdz9tlnO7dhTklJASAmJqbS2JiYGOe5lJQUvL29CQsLO+6Y6Ohol2dGR0c7x9QH2h5VRERc7Nu3j+7du3PPvfdyevfunHfeeYSFhbFhwwaysrIYOHCgyzUVJfkmDB0ymIKCArZu3VqpqdoRr7/+Om3atKnUL6EqUlNT6dqtG889+yy33XYbN990E+Hh4Vit+qtMRE4sMTGx0tSjE01dvPvuu9m4cSO///67yzmTyVTpd8MwXI790z/HHG18Ve7jTqooiIiIi6lTp5KXl8f+ffu46KKL+OCDD3jjjTcIDg7G19f3qNdYrVbm/PQTU6ZMof+AAfTr14/o6GhWrlzprEKUlpby6quvsWjRomrHZLFY6Nu3L127dgWgT58+tG3btuZvUkROKcHBwZVex0sUJkyYwPfff8+vv/5aafOE2NhYAJdv/dPS0pxVhtjYWMrKysjOzj7umNTUVJfnpqenu1QrPEmJgojIKa6goIA33niD/fv3c+jQIQzDYPr06cydO5f333+f4OBg2rRpg4+PD/Hx8fTv3/+E97x02DAwmcnMzOTMM89k3rx5QMU3ePPmzT1hyf9oIiMj+eH77yvN6RWRhsVwGB55VTk+w+Duu+/mm2++4ZdffiE+Pr7S+fj4eGJjY1mwYIHzWFlZGYsXL+ass84CoGfPnnh5eVUak5yczObNm51j+vXrR25uLqtWrXKOWblyJbm5uc4x9YF2PRIROUUZhsHy5cvZv38/H344kyuuuJyhF19MeVlZrXxTn56ezvbt27nxxlF8++1szjjjjBNfJCKN0pFdj3o+9q1Hdj1a8+zlVdr1aPz48XzyySd89913lXYeCgkJwc/PD6iouE6ZMoX333+fdu3a8dxzz/Hbb7+RkJBAUFAQAHfeeSc//vgjH3zwAeHh4Tz44INkZmayZs0aLBYLAEOHDiUpKcnZZ2bs2LG0bNmSH374oS7+GGpEFQURkVPU0qVLeee9DykoKODLL79g7NixlBQX07p16+Nel5+fz7Jly054/6ioKAYMGMD+/fv45Zdf+Pe//33MscuXL+eOO+6gqKiIr776ih07djjPZWdnU15eXvU3JiL1lyeqCdWoKMyYMYPc3FzOO+88mjRp4nx9/vnnzjEPP/wwEydOZPz48fTq1YtDhw4xf/58Z5IA8Morr3D55ZczYsQI+vfvj7+/Pz/88IMzSQD4+OOP6dq1K4MGDWLQoEF069aNWbNm1c6fcy3RCjARkVNU79692blzJ9deey2BgYEAVdq7++uvvybxUArt2rU76q4dRxMREQEcfYGew+Hg3XffZfe+RDIyMli6fAUbNmzg6aefBmD+ggXk5+Vx2223Ve2NiYjUUFUm2phMJiZPnszkyZOPOcbX15dp06Yxbdq0Y44JDw/no48+qkmYbqNEQUTkFOXn58ett95a7euGDRvGqlWrnEnC5CefxGI2H7dicPPNNx/zXEFBAe+99x4AH33yKeEhQdx3333O8x3at69yQiIi9ZvDMDC5eda7Q7Psa0xrFEREGqGVK1fyzjvv0KlTJ+6///4qXVNQUMDLL78MwEMPPeScj3uia46U26v610lycjLLli1j+PDheHl5AbBx40b27t3L7t27GTp0KB07dqzSvUSkYTiyRuGMSd9gcfMaBXtJIeuev7JGnZlPdaooiIg0MoWFhZw9YAC28nLCIyKrnCj8/PPPPPHEEwA0a9aMW2655YTXdOrc+bjn165dS3Z2NgMHDsThcNCyVSsOJiYSEBRM184v88cfFR1Iu3XrRrdu3aoUp4iIuIcWM4uINALZ2dns378fAH9/f047rT2YTGRlZrB9+/bjXutwOMjKyuLCCy9k8JChdO3W/agN1Y6mpLgEXz9/ioqKKh3Pzc0lKyuLlStXcSgpuWJsSQlhoaEAXDrsYlq0bFFp+8Di4mLad+jI66+/fvRnlZRUKSYRqb8MwwPbo2ryTI0pURARaeA+//xzwsPDiY+PJy8vD5PJxJ+rVzHg7LMBaN68+TGvNQyDzp27EBERwYoVK5g3dw4bN6ynZcuWx7wmJSXFuQvR1q1bKMjPc5mmlJqayvff/0B0dBSjR93IL7/8Qpeu3TiQeJDdu3fz6aefYvX248uvvnFeU15ejtXLmwMHDrg8Mz09nY4dO/Lqq6/qL30RETfR1CMRkQbu4UcmAeDr6+dcL/DMs88SHh7B4sWLCQg49nzgZ555hu0J2wmPiMDf3/+EzyopKWHmrI/p2qUTQ4cOJTIy8qjj2rVrR2lpKQcOHODrr7/m6quvdp5buHAhY8eO5ZEH78Pb29t5PDg4mF8Wzj+8Q9JfHA4Ho0aNYt++fbz11luMHTu2SusnRKT+qW4DtNp6ptSMKgoiIg1cixYtsHp58dFHszCZKrYgnT9/IRnZOZxzzjnHvdYwDDAMIiOj+PPPP0/4LB8fH+JbtaB3797HHWcymejatSvFxcXcc+9ELP5hANx+++2MGjUKqFiX0KFDh0rXxcTEYLVW/g7rhRde4Oeff8bPz48vvvhCSYKIiJuooiAi0gA5HA4WLlyI2Wxm7pyf8PX1rfQB+6cfv8fhcJzwPpdeeim//PILixcvdq5xOB6TycQ111xz3DGPPvooq//8k+KiIpYvX17p2ilTprB161ZsNht9+/Y94fOWLVvGv/71LwCmTZtGly5dTniNiIjUDiUKIiINjN1u5+YxY/jobx08Fy5cWGkBclX7Dgy//HISDxzgyiuvZMqUKbUS3y+//kZaegb79uxyHhs2bBhjxowhIiKCiy+5DIdhsHrFcvbu3cuaNWsqTU06IjMzk+uuuw673c7IkSOrtAuTiNRvDgeY3DwVqArfmcgxKFEQEWlgHn/88UpJQmBgEAUFBTW61/Dhl5OamsrHH81y9jQ4WSv+WE5aWhqLFy/GZDLRpk0bzjjjDOe0qDvvuN25g9EXX3zBnv0HGTJkiLM7NFRUTG6++WYOHjzIaaedxptvvum8XkRE3EMN10REGpDU1FRiY2MBeOKJJ3jsscdq7QN+bdmxYwcmk4mmTZuecIF0RkYG27ZtY8CAAZWOv/TSSzz44IP4+PiwYsUKTj/99DqMWETq2pGGa13u/xKLz4k3TqhN9tIiNr98jRqu1YAqCiIiDYjNZiMkJJQhQ4fw+OOPYzbXvz0p2rZtS0pKygmThKysLMLDw12ShJUrVzJpUsVOTq+88oqSBBERD1GiICLSgDRt2pTs7Kx6PQ3HbDYTFxd33DEZGRncfsd4epzelX//+9+Vzs2aNQubzcY111zDuHHj6jJUEXEzw1HxcvczpWaUKIiI1APbtm0jPj4eX1/fE46tz0lCVQUGBtLhtDYMHz7c5dy0adPo3r07I0aMaBTvVUSkodIaBRERD8vOziY8PByTyURBQUGVGp/VR9u2bWPp0qXceuutWCwWT4cjIvXIkTUKnSd6Zo3Clv9qjUJNqKIgIuJhBw8eBCA8PLzBJgkAmzZtYteefRQVFTk7RIuI/J3DYXhge1R9J15TShRERDyssLAQoMF/uB4xYgTDhw/Hx8fH06GIiEgtUKIgIuJhR3oK+Pn5eTiSk6ckQUSOx3AYGG7+ht/dz2tM6t++eiIipxgtFRMRkfpIiYKIiIcd+Rb+SGVBRESkPtDUIxERD4uJiQEqui43NFlZWRw6dIiuXbt6OhQRaQA09ahhUUVBRMTDoqOjASgqKiI/P9/D0VTPlOen8vwL//F0GCIiUgdUURAR8bCgoCCioqJIT08nISGBXr16eTqkKht4wfmaMiUiVeYwDExuXpfl0DqwGlOiICJSD3Ts2JH09HS2bdvWoBKFIUOGeDoEERGpI5p6JCJSD/Tq1Qur1UpKSoqnQ3Gxfft2XnzxRex2u6dDEZEG7sgaBXe/pGaUKIiI1AMPPfQQaWlpPPTQQ54OxcVTTz3F9Blvs2TJEk+HIiIibqREQUSkHoiNjSUsLMzTYRyVw+Hg4IH9bN682dOhiIiIG2mNgoiIHFNZWRnfzJ6Nw16Ow+HwdDgi0sAZhge2R9Vi5hpTRUFERI4qNzeXy6+8hqZxccBfjeFEROTUoERBRESOymw24+vrQ2xsLGDizjvv9HRIItLAGQ4Dh5tfWsxcc0oURETkqIKCgnj7zTfYnpAApopjhYWFng1KRETcRomCiIgcU2RkJMt+/524uKZERMWwZs0aT4ckIiJuosXMIiJyXJ06deL16dNYsWIFvXv39nQ4ItKAGYbh9sXFWsxcc6ooiIjICaWmppKSksLBgwc9HYqIiLiJKgoiInJca9as4dH/e4zsrEzmzJnLo49OoqioiMcee8zToYlIA+OJTslazFxzqiiIiMhxbdu2jbDQMJq1aMFtt93K4qXLmDNvgafDEhGROqaKgoiIHFerVq246KIL6dOnN7fccgtpaWkUFxd7OiwRaYAcDgPc/A2/QxWFGlOiICIix9W/f3+aNm1KfHw8ANHR0R6OSERE3EFTj0RE5LhMJpMzSRARkVOHKgoiIiIi4haGw47hsLv9mVIzqiiIiIiIiIgLVRRERERExC1UUWhYVFEQEREREREXShRERERERMSFph6JiIiIiFsYDocHph453Pq8xkQVBREROapZs2bx+OOPezoMERHxEFUURESkkvnz5+Pv78+jj/0bgKeeesrDEYlIY2HY7Rh2N1cU3Py8xkSJgoiIVPLF199i2MuJj4+naVyTSucOHTpE06ZNPRSZiIi4kxIFERGpZPwdtxEeHk7Lli2dxxITE3n66ad57733mD9/PhdccIEHIxSRhsowPLA9qqGKQk0pURARkUp69OgBQHl5OYsWLeK9997jm2++wX64fP/jjz8qURAROQUoURAREQAMw2Dfvn0sXryYH3/8kYULF5Kbm+s8f/755/PUU09x9tlnezBKERFxFyUKIiICwBtvvMHdd99d6Vh0dDTXXHMNt99+O927d/dQZCLSWKgzc8OiREFERABo0aIFVquVXr16MWjQIC6++GJ69+6N2aydtEVETkVKFEREBIDBgweTn5+Pr6+vp0MRkUZKFYWGRYmCiIgA4O3t7ekQRESkHlE9WUREREREXKiiICIiIiJuoalHDYsqCiIiIiIi4kIVBRERERFxC8Ph8EBFweHW5zUmqiiIiIiIiIgLJQoiIiIiIuJCU49ERERExC0cDju4eeqRQ4uZa0wVBRERERERcaGKgoiIiIi4hbZHbVhUURAREREREReqKIiIiIiIW6ii0LCooiAiIiIiIi6UKIiIiIiIiAtNPRIRERER97DbMcxungpk19SjmlJFQUREREREXKiiICIiIiJuYRjub7hmGKoo1JQqCiIiIiIi4kKJgoiIiIiIuNDUIxERERFxC8PhcP/UI4fDrc9rTFRREBERERERF6ooiIiIiIhbGA4PLGZWZ+YaU0VBRERERERcqKIgIiIiIm5RsUbBvWsGtEah5lRREBERERERF0oURERERETEhaYeiYiIiIhbaDFzw6KKgoiIiIiIuFBFQURERETcQhWFhkUVBRERERERcaFEQUREREREXGjqkYiIiIi4hcNhx6SpRw2GKgoiIiIiIuJCFQURERERcQvD7gCTmysKdnVmrilVFERERERExIUqCiIiIiLiFobhge1RDa1RqClVFERERERExIUSBRERERERcaGpRyIiIiLiFobD7v7FzNoetcZUURAREREREReqKIiIiIiIW6ii0LCooiAiIiIi8jdvvPEG8fHx+Pr60rNnT5YuXerpkDxCiYKIiIiIyGGff/45EydO5LHHHmPdunUMGDCAoUOHcuDAAU+HVi1fffUVXbt2xc/Pj4iICC688EIKCwud59977z06d+6Mj48PTZo04e6773aeO3DgAMOHD1eiICIiIiLuYTjsHnlVx8svv8ytt97KbbfdRseOHfnvf/9L8+bNmTFjRh39qdS+5ORkrr/+em655Ra2bdvGb7/9xpVXXolhGADMmDGDu+66i7Fjx7Jp0ya+//572rZtC4BhGFx++eVkZWVpjYKIiIiIuIm9HMMDzwTIy8urdNjHxwcfH59Kx8rKylizZg2TJk2qdHzQoEEsX768buOsRcnJydhsNq688kpatmwJQNeuXZ3nn3nmGR544AHuvfde57HevXsDsHDhQjZu3MjevXuVKIiIiIhI3fL29iY2NpaUrV945PmBgYE0b9680rEnnniCyZMnVzqWkZGB3W4nJiam0vGYmBhSUlLqOsxa0717dwYOHEjXrl0ZPHgwgwYN4uqrryYsLIy0tDSSkpIYOHDgUa/dtm0bzZs3p3nz5koURERERKRu+fr6snfvXsrKyjzyfMMwMJlMlY79s5rwd/8ce7Tr6zOLxcKCBQtYvnw58+fPZ9q0aTz22GOsXLmSyMjI41779/eqREFERERE6pyvry++vr6eDuO4IiMjsVgsLtWDtLQ0lypDfWcymejfvz/9+/fn8ccfp2XLlsyePZv777+fVq1asWjRIs4//3yX6zp16sSBAwdITExUoiAiIiIiAhVTpHr27MmCBQu44oornMcXLFjA8OHDPRhZ9axcuZJFixYxaNAgoqOjWblyJenp6XTs2BGAyZMnM27cOKKjoxk6dCj5+fksW7aMCRMmcOGFF9KtWzduuOEGJQoiIiIiIkfcf//9jBo1il69etGvXz/efvttDhw4wLhx4zwdWpUFBwezZMkS/vvf/5KXl0fLli156aWXGDp0KAA33XQTJSUlvPLKKzz44INERkZy9dVXAxWViG+//ZYJEyZgMo7skyQiIiIiIrzxxhu88MILJCcn06VLF1555RXOOeccT4fldkoURERERETEhRquiYiIiIiICyUKIiIiIiLiQomCiIiIiIi4UKIgIiIiIiIulCiIiIiIiIgLJQoiIiIiIuJCiYKIiIiIiLhQoiAiIiIiIi6UKIiIiIiIiAslCiIiIiIi4kKJgoiIiIiIuFCiICIiIiIiLpQoiIiIiIiICyUKIiIiIiLiQomCiIiIiIi4UKIgIiIiIiIulCiIiIiIiIgLJQoiIiIiIuJCiYKIiIiIiLhQoiAiIiIiIi6UKIiIiIiIiAslCiIiIiIi4kKJgoiIiIiIuFCiICIiIiIiLpQoiIiIiIiICyUKIiIiIiLiQomCiIiIiIi4UKIgIiIiIiIulCiIiIiIiIgLJQoiIiIiIuJCiYKIiIiIiLhQoiAiIiIiIi6UKIiIiIiIiAslCiIiIiIi4kKJgoiIiIiIuFCiICIiIiIiLpQoiIiIiIiICyUKIiIiIiLiQomCiIiIiIi4UKIgIiIiIiIulCiIiIiIiIgLJQoiIiIiIuJCiYKIiIiIiLhQoiAiIiIiIi6UKIiIiIiIiAslCiIiIiIi4uL/AW0ZuKBXnaMZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, gax = plt.subplots(figsize = (10,8))\n",
    "\n",
    "geo_merge.plot(\n",
    "    ax=gax, edgecolor='black', column='weighted_STR', legend=True, cmap='RdBu_r',\n",
    "    vmin=geo_merge['weighted_STR'].min(), vmax=geo_merge['weighted_STR'].max()\n",
    ")\n",
    "\n",
    "\n",
    "gax.annotate('score tourist ratio',xy=(0.76, 0.06),  xycoords='figure fraction')\n",
    "\n",
    "plt.axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df22897-3a5d-4e3b-a22a-5de84273c530",
   "metadata": {},
   "source": [
    "The most obscure but worth visiting provincial-level administrative division is Tibet."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "test",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
